Data Engineer ATS Keywords for Production Pipelines (2026)

Data Engineer ATS Keywords for Production Pipelines (2026)

Data Engineer (DE) hiring is a different keyword target than Data Analyst, Analytics Engineer, or BI Developer hiring, and most resume advice conflates the four. Recruiters at tech companies — Stripe, Snowflake, Databricks, Airbnb, Netflix, Confluent, dbt Labs — configure ATS searches for DE roles around four signal classes that don't appear on BI or analyst resumes: production-pipeline ownership (on-call rotation, SLA, freshness, volume), warehouse + modeling discipline (Snowflake/BigQuery/Redshift/Databricks, dbt, Kimball, Iceberg), orchestration fluency (Airflow, Dagster, dbt, Prefect), and distributed-processing experience (Spark, Trino, Flink, Kafka). A resume that reads like an analyst resume with "engineer" in the title gets filtered out for DE roles because the keyword density on those four classes is too low [1][2]. This page lists the DE keywords that pass screens in 2026, grouped by signal class, with worked rewrites and a counter-list of analyst-flavored keywords that backfire when a DE resume leans on them.

Key Takeaways

• DE resumes are scanned for four signal classes — production-pipeline ownership, warehouse + modeling discipline, orchestration fluency, and distributed-processing experience — that most BI/analyst resumes have zero density on; missing all four is the #1 reason analysts get filtered out of DE searches [3][4].
• Quantified pipeline numbers ("ingested 12 TB/day," "P95 freshness under 15 minutes," "owned 47 dbt models across 3 marts") are the highest-leverage Tier-1 DE keywords because Greenhouse, Lever, and Ashby all weight quantified production scope above unquantified prose [2][5].
• Joe Reis and Matt Housley's Fundamentals of Data Engineering frames the role around the data engineering lifecycle — generation, ingestion, storage, transformation, serving — and the DE resume keyword surface mirrors that lifecycle: ingestion (Kafka, Debezium, Fivetran), storage (S3, GCS, Iceberg, Delta), transformation (Spark, dbt, SQL), serving (Snowflake, BigQuery, BI handoff) [3].
• Maxime Beauchemin's "rise of the data engineer" framing is canonical for the discipline: DEs build production data systems, not dashboards, and the keyword cluster reflects that — Airflow, schedulers, idempotency, backfills, data contracts, lineage [6].
• "dbt," "Airflow," "Snowflake," "Spark," "Kafka," and "Iceberg/Delta" are Tier-1 keywords whose absence reads as "BI developer with a fancier title," not DE [3][6][7].
• BLS does not publish a dedicated Data Engineer SOC code; the two closest proxies are SOC 15-1242.00 Database Architects (median annual wage $141,210, May 2024) and SOC 15-1252.00 Software Developers ($133,080, May 2024). Both should be cited as proxies, not as ground truth — levels.fyi tracks Data Engineer comp at top-tier tech companies separately and consistently above the BLS proxies because BLS does not isolate the modern DE role [8][9][10].
• "On-call," "SLA," "freshness," and "incident" are themselves Tier-1 DE keywords — recruiters scan for evidence of production-system ownership, because pipeline reliability work is the rarest and most-screened DE signal [3][6].

How Data Engineer ATS Screens Work

DE hiring runs through the same ATS engines as software-engineer and data-analyst hiring — Greenhouse, Lever, Workday, Ashby, SmartRecruiters, iCIMS — but the keyword matrix is its own. Where an analyst search filters on SQL, dashboarding, and BI tools (Looker, Tableau, Power BI), a DE search filters on production-pipeline ownership, warehouse + modeling discipline, and orchestration, with SQL fluency as a baseline assumption rather than a differentiator. The DE ATS scan is mostly looking for evidence that the candidate operates production systems: a candidate whose top three bullets describe ad-hoc dashboards or one-off SQL queries will be filtered out for senior DE roles, even if the title is right [3][6].

Engine-specific behavior for DE hiring:

Greenhouse (used at Stripe, Airbnb, Snowflake, Databricks, dbt Labs, and most Series-B-and-up data-tooling startups) supports semantic matching, so "owned the Airflow DAGs for the analytics warehouse" registers as related to "operated the orchestration layer for analytics" or "ran the ETL scheduler" [2]. Greenhouse weights experience-bullet keywords more than skills-section keywords for DE roles — production-system bullets carry the load. The recruiter UI lets the filter "production data systems experience within last 2 years" return only candidates whose recent role had owner-on-call structure [2].

Lever (used at Eventbrite, Shopify, parts of Lyft) emphasizes recency. For DE roles specifically, Lever recruiters often filter by "production-pipeline experience within last 2 years" — a candidate who pivoted from DE to analyst and is now applying back to DE roles needs to surface production-systems work in the most recent 24 months prominently [2].

Workday (used at Disney, Salesforce, Adobe, large-enterprise DE hires) is the strictest exact-match parser. For DE, Workday filters often require literal strings — "Apache Airflow," "Snowflake," "AWS Glue," "Apache Spark" — exactly as written. A candidate who lists "Airflow" but not "Apache Airflow" can fall out of strict Workday filters [11]. The fix: write the canonical product name once at minimum.

Ashby (used at Notion, Linear, Ramp, Anthropic, and most modern AI-era startups) is the friendliest ATS for nuanced DE resumes because its LLM-based scoring reads bullets and infers level from context. A bullet that describes "owned the Airflow → Snowflake → dbt analytics pipeline for the platform team, including weekly on-call rotation, freshness SLAs at P95 under 15 minutes, and incident-review participation" registers as senior DE signal even if the title is ambiguous [12]. Ashby is where DE-with-mixed-titles ("Software Engineer (Data)," "Analytics Engineer," "Platform Engineer") gets the fairest read.

SmartRecruiters (Visa, Atlassian) and iCIMS (Capital One, Disney non-engineering) lean stricter and more exact-match. Both score the title block heavily for DE searches, and both penalize creative titles ("Pipeline Engineer," "Data Platform Engineer," "Analytics Infrastructure") for not matching the canonical "Data Engineer" string. Taleo (legacy enterprise, Oracle) is the oldest and the strictest; for DE Taleo searches, write defensively with explicit phrases like "Data Engineer," "ETL," "data pipeline," "data warehouse" alongside any modern terminology [11].

Tier 1 — Languages

These are the canonical DE programming languages. Cite the ones you actually ship production code in; do not stuff the section with languages you've only touched in a tutorial [3][6].

Skip JavaScript, R, Ruby, and Go from the DE language section unless you've shipped production data infrastructure in them. R on a DE resume reads as analyst signal; JavaScript reads as confused.

Tier 1 — Orchestration

Orchestration is the core DE keyword cluster — recruiters filter heavily on it because pipeline-orchestration competence is what separates DEs from analysts [3][6][7].

If you have shipped Airflow + dbt + one warehouse, that is the modern-DE core stack and should appear in your top 5 keywords by surface area.

Tier 1 — Warehouses

Warehouse fluency is heavily weighted; cite every cloud warehouse you've owned models in [7][13].

Tier 1 — Distributed Processing Engines

Distributed-processing experience separates senior DE from junior — recruiters at Snowflake, Databricks, Airbnb, Netflix all filter explicitly on it [4][7].

Tier 1 — Streaming & CDC

Streaming experience is a strong differentiator — most DE candidates have batch experience only [3][6].

Tier 1 — Data Quality & Observability

Data-quality discipline is the rising DE signal in 2026 [3][14]. Recruiters increasingly scan for explicit DQ tooling.

Tier 1 — Cloud & Storage

Tier 1 — Modeling & Table Formats

Modeling discipline is what hiring managers probe in interviews — surface it explicitly on the resume so it doesn't get filtered out before the conversation [13][15].

Tier 1 — Production Ownership Verbs & Numbers

Production-system ownership is the single rarest DE signal. The keyword cluster here separates senior DE from mid [3][6][14].

Owned — Accountability verb. Patterns: "Owned the analytics warehouse for the platform team across 4 quarters," "Owned the Kafka cluster for the order-events stream — 12 brokers, 80 topics, 6 consumer-group teams." "Owned" reads as DE; "contributed to" reads as junior.

On-call — Production-reliability keyword. Patterns: "Weekly on-call rotation across 4 DEs for the analytics warehouse," "Owned the on-call playbook for the streaming-pipeline incident class." Hiring managers filter explicitly on this — DEs who have never been on-call read as non-production [3][6].

SLA / SLO / SLI — Reliability vocabulary. Patterns: "Authored and met the freshness SLA (P95 under 15 minutes) for the analytics-warehouse loads across 4 quarters," "Owned the SLO for the order-events stream — 99.95% availability across the consumer-group set."

Freshness — Pipeline-quality keyword. Patterns: "Cut median freshness from 6 hours to 11 minutes across the analytics marts after rebuilding the orchestration layer," "Tracked freshness SLI per dbt model with Monte Carlo, with PagerDuty alerts on breach."

Volume — Scale signal. Patterns: "Ingested 12 TB/day from the order-events stream," "Operated 47 dbt models across 8 marts with 240 GB nightly load." Numbers are the requirement; "high-volume" without a number is anti-signal.

Backfill — Operational-fluency keyword. Patterns: "Authored idempotent backfill protocols for the 47 dbt models, including partition-pruned reprocessing for the historical 18 months of data," "Ran 9 production backfills in the warehouse-migration window without downstream consumer impact."

Incident — Production-system experience. Patterns: "Led 4 post-mortems for warehouse-load incidents in 2025; authored the durable-fix protocol that cut repeat-incident rate by 70%," "Participated in the org-wide on-call rotation; closed 12 P2 incidents in the year." Use real numbers if cited.

Lineage — Modern-DE keyword. Patterns: "Adopted OpenLineage across the Airflow + dbt + Snowflake stack for end-to-end column-level lineage," "Owned the dbt exposures + Atlan integration for downstream BI lineage."

Idempotent / idempotency — Engineering-rigor keyword [3][6]. Patterns: "Refactored the legacy nightly-load to idempotent partition-overwrite semantics, eliminating the duplicate-row class of incidents." Senior-DE signal.

Schema evolution / data contract — Modern-DE governance keyword [14]. Patterns: "Owned the data-contract definition for the order-events stream between Product Engineering and Analytics — schema, semantics, ownership, and breaking-change protocol."

Counter-List — Keywords That Backfire on DE Resumes

This is the part most resume advice misses. DE resumes can be sunk by analyst-flavored keywords that read as "BI-developer-with-fancier-title" signal [3][6][7].

"Built dashboards" / "Created reports" / "Built KPI report" — Analyst verbs. On a senior DE resume, dashboard-leading bullets read as career regression. Reframe: "Owned the dbt marts that power the leadership-team activation dashboards; the dashboard surface was owned by the BI team, the underlying models and freshness contracts were mine."

Tableau / Power BI / Looker as primary skills — BI-tool framing. DE resumes that lead the Skills section with three BI tools and no warehouse name read as analyst. Mention BI tools once if relevant ("BI handoff to Looker explores via dbt exposures") — never in the top three skill bullets.

"Excel" / "Pivot tables" / "VLOOKUP" — Junior-analyst signal. Skip entirely on DE resumes; nothing about Excel on a modern DE resume reads as DE signal.

"ETL" without specifics — Generic. The literal string "ETL" appears in many DE postings, so cite it once for ATS coverage, but never as the only descriptor of your work. Pair it with the orchestrator name, the warehouse name, the volume, and the SLA. "Built ETL pipelines" is 1995 framing; "Owned the Airflow + dbt + Snowflake ELT stack for the analytics warehouse" is 2026 framing.

"Data wrangling" / "Data cleaning" — Analyst verbs. Skip on DE resumes; these read as one-off-script work, not production-system work.

"SAS" / "SPSS" / "MATLAB" — Statistician-stack signal. Reads as "career data scientist applying for DE roles" rather than DE. Skip unless you've shipped production data infrastructure in them (you haven't).

"R" as a primary language — Analyst signal. R can appear in a "secondary languages" mention if relevant, but not as a top-three skill on a DE resume.

"Big Data" as a buzzword — 2014 framing. Replace with the actual scale — "12 TB/day," "47 dbt models," "8 brokers, 80 topics" — that the recruiter can calibrate.

Long unfocused tools list (20+ items) — Spam-trigger on Greenhouse and Ashby [2][12]. A DE Skills dump that lists every cloud-data product trips IC-not-yet-DE / generalist-not-yet-specialist signal. Limit Skills to 16–20 items across language / orchestration / warehouse / streaming / cloud / modeling / data-quality categories.

"ETL Developer" title without modern-stack evidence — Pure-Informatica / SSIS / DataStage shops. The title is fine, but pair it with at least one modern-stack keyword cluster (Airflow, dbt, Snowflake, Spark) somewhere in the resume to avoid the "legacy ETL only" filter at modern-stack companies.

"Just-Excel," "ad-hoc SQL queries," "occasional Python script" — Implicit-junior framing. If those are the strongest verbs in your bullets, the resume reads as analyst-with-some-engineering-curiosity, not DE.

No production-deployment evidence — DE resumes that describe pipelines without ever mentioning production, on-call, SLA, freshness, or incident read as homework / coursework. Production-system ownership is the rarest, most-screened DE signal — its absence is a screen failure regardless of the rest of the resume [3][6].

Worked Examples — DE Keywords in Experience Bullets

Example 1 — Pipeline ownership

Before (C-grade): Built data pipelines for the analytics team.

After (A-grade): Owned the Airflow + dbt + Snowflake analytics pipeline across the platform-data team — 47 dbt models, 80+ tests, 12 TB/day ingestion, weekly on-call rotation across 4 DEs, and freshness SLA at P95 under 15 minutes across 4 quarters.

Keywords hit: Airflow, dbt, Snowflake, on-call, SLA, freshness, P95, dbt tests, ingestion, volume.

Example 2 — Streaming & CDC

Before: Worked on streaming pipelines from Postgres.

After: Authored Debezium-based CDC pipeline from Postgres → Kafka → Snowflake — replaced the legacy nightly-dump ingestion, cut median freshness from 18 hours to 9 minutes for the orders fact table, and authored the schema-evolution / data-contract protocol with Product Engineering.

Keywords hit: Debezium, CDC, Postgres, Kafka, Snowflake, freshness, schema evolution, data contract.

Example 3 — Distributed processing

Before: Used Spark for big data jobs.

After: Authored PySpark jobs on Databricks for the high-volume event-aggregation pipeline (12 TB/day) — tuned shuffle partitions and broadcast joins for 4× speedup, migrated underlying tables from Parquet to Delta with OPTIMIZE / ZORDER cadence, and owned the on-call rotation for the workload.

Keywords hit: PySpark, Databricks, volume, shuffle partitions, broadcast join, Delta, OPTIMIZE, ZORDER, on-call.

Example 4 — Modeling & warehouse discipline

Before: Designed warehouse tables and dbt models.

After: Designed Kimball-style star schemas for the finance and product marts — 6 fact tables and 12 conformed dimensions, implemented in dbt with the staging / intermediate / marts pattern, SCD Type 2 on the customer dimension, and 200+ dbt tests for relational and accepted-values invariants.

Keywords hit: Kimball, star schema, fact, dimension, dbt, staging, intermediate, marts, SCD Type 2, dbt tests.

Example 5 — Data quality & observability

Before: Improved pipeline reliability.

After: Onboarded Monte Carlo for warehouse-table observability across 4 marts and adopted Great Expectations for upstream-API ingestion validation — cut median freshness-incident detection from 4 hours to 6 minutes and reduced the duplicate-row incident class to zero across the year.

Keywords hit: Monte Carlo, observability, Great Expectations, freshness, incident.

Example 6 — Cloud & storage

Before: Worked with cloud storage.

After: Owned the AWS data-lake organization — S3 Iceberg tables across raw / staging / curated zones, EMR for the legacy Spark jobs, Glue for the catalog, and Trino for federated query across S3 and the Postgres OLTP read-replica.

Keywords hit: AWS, S3, Iceberg, EMR, Spark, Glue, Trino, Postgres, federated query.

Density and Placement Rules for DE

1. Professional Summary: Pack 5–6 Tier-1 DE keywords here. Example: "Senior Data Engineer with 7 years of production-pipeline experience — owned the Airflow + dbt + Snowflake analytics warehouse, ingested 12 TB/day from Kafka via Debezium CDC, weekly on-call across 4 DEs, freshness SLA at P95 under 15 minutes."
2. Skills section: 4 categories, 16–20 items total. Languages (Python, SQL, Scala or Java), Orchestration (Airflow, dbt, Dagster), Warehouse + Storage (Snowflake / BigQuery / Redshift / Databricks, S3 / GCS, Iceberg / Delta), Streaming + Quality (Kafka, Debezium, Great Expectations or Monte Carlo). Skip the 30-item buzzword dump.
3. Experience bullets: Each recent bullet should pair a production-ownership verb with a quantified outcome (volume, freshness, model count, incident reduction). Aim for 1 Tier-1 DE keyword per bullet, embedded naturally — not stuffed.
4. Don't: Mix analyst-flavored bullets with DE-pipeline bullets in the same role. Pick one framing per role and commit. Mixed framing reads as confused career level.
5. Repository or project link. Senior DE roles increasingly expect a public link — a GitHub project, a dbt project, an Airflow DAG sample, a blog post — that demonstrates production-engineering taste. Not portfolio-style; a code-review-able artifact.

Density rule of thumb for DE: Tier-1 production-ownership keywords (owned, on-call, SLA, freshness, volume) appear 4–6 times across the resume. Tier-1 stack keywords (Airflow, dbt, warehouse name, Spark or Kafka) appear 2–4 times each. Tier-1 modeling keywords (Kimball, dimensional, dbt staging / intermediate / marts, Iceberg or Delta) appear 2–3 times. Generic tools-list dumps are a screen failure — depth-on-the-stack-you've-shipped beats breadth.

Anti-Patterns That Fail DE Screens

• The "BI-developer-with-fancy-title" resume: Bullets are 80% dashboard / report / KPI, 20% pipeline. Reads as analyst, not DE. Recruiters at modern-stack companies filter against this aggressively for senior DE roles [3][6].
• No production-system evidence: Resume describes 4 years of "data engineering" but never names on-call, SLA, freshness, incident, or backfill. Reads as homework / consultancy / one-off-projects work [3].
• No volume numbers: "Built large-scale data pipelines." How large? The number is the signal. "12 TB/day," "47 dbt models," "80 Kafka topics" calibrate level instantly; "large-scale" does not [4].
• "ETL" without modern-stack vocabulary: Resume mentions ETL, data warehouse, and pipelines but never names Airflow, dbt, Snowflake, BigQuery, Spark, Kafka, Iceberg, or Delta. Reads as legacy-Informatica / DataStage / SSIS — fine for some roles, but a screen failure for modern-stack postings [13].
• 30-item tools list: "Python, R, Scala, Java, SQL, Bash, Airflow, Prefect, Dagster, dbt, Snowflake, BigQuery, Redshift, Databricks, Spark, Flink, Trino, Presto, Kafka, Pulsar, Kinesis, Pub/Sub, S3, GCS, ADLS, AWS, GCP, Azure, Tableau, Power BI, Looker, Excel, ..." trips spam-detection on Greenhouse and Ashby and reads as "no actual depth on any of them" [2][12].
• Title inflation: Calling a 2-month dbt-stretch role "Senior Data Engineer." Hiring managers cross-check at interview, and the gap shows fast.
• "I personally" first-person SQL framing: "I queried the database." Junior. Replace with the production framing: "Owned the warehouse-load SQL across 47 dbt models in Snowflake."
• Pipeline-as-magic framing: "Pipelines just work." DE resumes that don't describe operational discipline — backfills, idempotency, schema evolution, on-call — read as junior even with the right tool names.

FAQ

I'm a data analyst applying for my first DE role — how do I write this resume?

Lead with the proto-engineering work you've already done — dbt models you've authored, SQL refactors you've shipped, Airflow DAGs you've maintained, Python scripts you've productionized — and frame each in DE-resume language. "Authored 12 dbt models on top of the Snowflake warehouse for the marketing-attribution dashboards; refactored the slowest mart from 11-minute to 90-second build, added 30+ dbt tests for accepted-values and uniqueness invariants, and partnered with the data-platform team on production deployment." That bullet hits "dbt," "Snowflake," "warehouse," "dbt tests," "production deployment" — Tier-1 DE keywords from an analyst role. Joe Reis and Matt Housley's Fundamentals of Data Engineering Chapter 1 (the data-engineering lifecycle) is the canonical scaffolding for this transition framing [3].

Should I list BI tools (Tableau, Looker, Power BI) on a DE resume?

Once, briefly, in a "BI handoff" mention — not as a top-three skill. The DE ATS scan does not weight BI fluency heavily, and a long BI-tool list reads as analyst signal. The exception: small startups where the DE is expected to ship some dashboards. Even there, list the tool once in a single line, not as a section. Looker fluency is sometimes assumed for DEs who own dbt exposures; restating it adds little signal.

How do I handle a BI / analyst → DE pendulum on my resume?

Frame the analyst stretch as deliberate context-building and the DE move as current. Pattern: "Spent 18 months as a senior analyst on the marketing-attribution team to build domain depth before moving to data engineering — returned to a DE role on the platform-data team Q3 2024." Recruiters at modern data-tooling companies (Snowflake, Databricks, dbt Labs, Confluent) read pendulum moves as senior signal, not as a red flag — but only if framed as deliberate and current work is engineering. The trap is hiding the analyst period.

How many years do I need to claim "Data Engineer"?

The honest floor is 1–2 years of dedicated production-pipeline work, with at least 6 months of on-call ownership. Below that, the role is closer to "analytics engineer" or "BI developer / data-platform engineer" and the resume should frame it accordingly. The signal hiring managers want is "has owned at least one production pipeline through one major change (migration, schema evolution, incident class) and one full on-call quarter" — that's roughly the 12-month, single-pipeline mark. Below it, the resume reads as transitional, which is fine if framed honestly.

Do I need a portfolio or a public project as a data engineer?

Increasingly, yes. The expectation in 2026 is one of: a public dbt project, an Airflow DAG sample, a Spark job repo, or a technical blog post — something a hiring manager can read for engineering taste. Per Maxime Beauchemin's framing of the modern DE role (build production systems, not dashboards), the portfolio for a DE should show selected pipeline-engineering work, not visualizations [6]. Even one good repo + one technical blog post is plenty; depth beats breadth.

How do I list a DE role where I also did analytics work?

Lead the bullet cluster with engineering work; close with one bullet on analytics partnership if relevant. Pattern: 4 pipeline / orchestration / warehouse bullets, then 1 bullet like "Partnered with the analytics team on the marketing-attribution dashboards — owned the underlying dbt marts and freshness SLA, the dashboard surface was owned by the BI team." Don't lead with the analytics work; doing so signals analyst-pretending-to-be-DE.

What about Data Platform Engineer or Analytics Engineer roles?

The keyword sets diverge from generic DE. For Analytics Engineer, lean harder on dbt, warehouse modeling (Kimball, staging / intermediate / marts), and BI handoff (exposures, semantic layer); de-emphasize Spark / Kafka / Flink. For Data Platform Engineer, lean harder on infrastructure (Kubernetes, Terraform, internal-tooling, self-service-platform), Spark on K8s, and orchestrator development; the warehouse-modeling section is lighter. Both still need on-call, SLA, and incident keywords [3][14].

What does the BLS say about Data Engineer pay?

BLS does not publish a dedicated SOC code for Data Engineer. The two closest BLS proxies are SOC 15-1242.00 Database Architects (median annual wage $141,210, May 2024) and SOC 15-1252.00 Software Developers (median annual wage $133,080, May 2024) [8][9]. Both should be treated as proxies, not ground truth. Industry-specific salary trackers — levels.fyi, Built In, Glassdoor — typically report Data Engineer comp at top-tier tech companies above the BLS proxies because BLS does not isolate the modern DE role from broader software-engineering categories [10].

References

[1] Greenhouse Software. "Sourcing and Filtering Best Practices — Greenhouse Help Center." https://support.greenhouse.io/hc/en-us/articles/360051506331-Sourcing-best-practices

[2] Ashby HQ. "How Ashby's AI-Powered Sourcing Works." https://www.ashbyhq.com/resources/guides/ai-powered-sourcing

[3] Joe Reis and Matt Housley. Fundamentals of Data Engineering: Plan and Build Robust Data Systems (O'Reilly, 2022). https://www.oreilly.com/library/view/fundamentals-of-data/9781098108298/

[4] dbt Labs. "Modern Data Stack & the Analytics Engineering Workflow." https://www.getdbt.com/blog/

[5] levels.fyi. "Data Engineer Compensation Data." https://www.levels.fyi/t/data-engineer

[6] Maxime Beauchemin. "The Rise of the Data Engineer." https://maximebeauchemin.medium.com/the-rise-of-the-data-engineer-91be18f1e603

[7] Apache Airflow Documentation. "Concepts — DAGs, Operators, Schedulers." https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/index.html

[8] U.S. Bureau of Labor Statistics. "Database Architects — SOC 15-1242, Occupational Employment and Wage Statistics, May 2024." https://www.bls.gov/oes/current/oes151242.htm

[9] U.S. Bureau of Labor Statistics. "Software Developers — SOC 15-1252, Occupational Employment and Wage Statistics, May 2024." https://www.bls.gov/oes/current/oes151252.htm

[10] levels.fyi. "Software Engineer / Data Engineer comparative compensation." https://www.levels.fyi/

[11] Workday. "Workday Recruiting — Candidate Search Documentation." https://doc.workday.com/admin-guide/en-us/staffing/recruiting/candidate-experience.html

[12] Ashby HQ. "Recruiting Workflow and Candidate Scoring." https://www.ashbyhq.com/

[13] Ralph Kimball and Margy Ross. The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition (Wiley, 2013). https://www.kimballgroup.com/data-warehouse-business-intelligence-resources/books/data-warehouse-dw-toolkit/

[14] dbt Labs. "Data Contracts and the Modern Data Stack." https://www.getdbt.com/blog/data-contracts

[15] Apache Iceberg. "Table Format Specification." https://iceberg.apache.org/spec/