Data Engineer Job Description: Duties, Skills, Salary, and Career Path

The Bureau of Labor Statistics projects 4 percent employment growth for database administrators and architects — the classification that includes data engineers — from 2024 to 2034, but this headline number understates the demand: data engineering job postings on LinkedIn and Indeed have grown at three to four times that rate as organizations invest in building the data infrastructure required for AI and machine learning initiatives [1].

Key Takeaways

• Data engineers design, build, and maintain the data pipelines, warehouses, and infrastructure that enable organizations to collect, store, transform, and serve data at scale.
• The median annual wage for database architects was $135,980 in May 2024; data engineers with pipeline and cloud specialization typically earn within this range, with senior practitioners exceeding $180,000 in total compensation [1].
• Most positions require a bachelor's degree in computer science, software engineering, or a related field, with strong emphasis on SQL, Python, and distributed systems.
• Core competencies include ETL/ELT pipeline development, data modeling, cloud data platform management (Snowflake, Databricks, BigQuery), and workflow orchestration.
• The role bridges software engineering and data science — data engineers build the infrastructure that data scientists, analysts, and machine learning engineers depend on to do their work.

What Does a Data Engineer Do?

A data engineer builds and maintains the highways that data travels on. While data scientists analyze data and build models, and data analysts create dashboards and reports, the data engineer ensures that data arrives at the right place, in the right format, at the right time.

The daily work centers on pipeline development. A data engineer designs workflows that extract data from source systems (application databases, third-party APIs, event streams, file drops), transform it (cleaning, deduplication, schema mapping, aggregation), and load it into a destination system (data warehouse, data lake, feature store). These ETL or ELT pipelines run on schedules or in response to events and must handle failures gracefully — retrying transient errors, alerting on persistent failures, and maintaining data quality throughout.

Data modeling is a core responsibility. Data engineers design the table structures and relationships in the data warehouse, choosing between dimensional modeling (star schemas, fact and dimension tables), normalized models, or wide denormalized tables based on query patterns and analytical needs. According to O*NET, database architects — a closely related role — "design strategies for enterprise databases, data warehouse systems, and multidimensional networks" and "develop and implement data models for warehouse infrastructure" [2].

Infrastructure management occupies significant time. Data engineers provision and configure cloud data platforms (Snowflake, Databricks, BigQuery, Redshift), set up data lake storage (S3, GCS, ADLS), manage Spark clusters for large-scale processing, and tune query performance by analyzing execution plans and optimizing partitioning strategies.

Data quality is the data engineer's perpetual concern. They implement validation checks at each pipeline stage — schema validation, null checks, uniqueness constraints, referential integrity, and statistical anomaly detection. Tools like Great Expectations, dbt tests, and Monte Carlo help automate data quality monitoring. When data quality degrades, the data engineer traces the issue to its source and fixes it before downstream consumers are affected.

Collaboration is constant. Data engineers work with data scientists to build feature pipelines for ML models, with analysts to ensure their dashboards have clean and timely data, with application developers to instrument event tracking, and with data platform teams to manage shared infrastructure.

Core Responsibilities

Primary duties, consuming approximately 60 percent of working time:

1. Design and build data pipelines that extract data from operational databases, APIs, event streams, and file systems, transform it according to business rules, and load it into analytical destinations.
2. Develop and maintain data models in the data warehouse, designing schemas that balance query performance, storage efficiency, and analyst usability.
3. Manage cloud data infrastructure including data warehouses (Snowflake, BigQuery, Redshift), data lakes (S3/GCS with Delta Lake or Iceberg), compute clusters (Spark, Databricks), and streaming platforms (Kafka, Kinesis) [2].
4. Implement data quality frameworks with automated validation, anomaly detection, and alerting to catch data issues before they affect downstream consumers.
5. Optimize pipeline and query performance by analyzing execution plans, adjusting partitioning and clustering strategies, managing materialized views, and tuning resource allocation.
6. Build and manage workflow orchestration using tools like Apache Airflow, Dagster, or Prefect to schedule, monitor, and manage pipeline dependencies.

Secondary responsibilities, approximately 30 percent of time:

7. Develop streaming data architectures for real-time use cases using Apache Kafka, AWS Kinesis, Google Pub/Sub, or Apache Flink.
8. Implement data governance and cataloging using tools like Alation, Collibra, or Datahub to enable data discovery, lineage tracking, and access control.
9. Build feature engineering pipelines for machine learning teams, transforming raw data into features and serving them to model training and inference systems.
10. Develop and maintain dbt (data build tool) projects for SQL-based transformations, implementing version-controlled analytics engineering workflows [3].

Administrative and organizational activities, approximately 10 percent:

11. Document data architecture, pipeline logic, and data dictionaries to enable self-service data consumption by analysts and scientists.
12. Participate in on-call rotations for data platform reliability, responding to pipeline failures, data freshness alerts, and infrastructure issues.
13. Mentor junior data engineers and contribute to engineering standards, code review practices, and architectural decision records.

Required Qualifications

Most data engineer positions require a bachelor's degree in computer science, software engineering, mathematics, or a related technical field. Some employers accept equivalent experience in software engineering or data analysis in lieu of a degree.

Experience requirements follow a tiered structure. Entry-level data engineers need one to three years of software engineering or data-related experience. Mid-level roles require three to six years with demonstrated experience building production pipelines. Senior data engineers need six or more years with expertise in designing data architectures, mentoring other engineers, and making infrastructure decisions.

Technical requirements are specific:

• Advanced SQL: window functions, CTEs, query optimization, schema design
• Python programming with data libraries (Pandas, PySpark) and scripting for pipeline logic
• Experience with at least one cloud data platform: Snowflake, Databricks, BigQuery, or Redshift
• Understanding of data modeling: dimensional modeling, star schemas, slowly changing dimensions
• Experience with workflow orchestration: Apache Airflow, Dagster, or Prefect
• Familiarity with version control (Git) and CI/CD practices for data pipelines
• Understanding of distributed computing concepts (partitioning, shuffling, parallelism) [2]

Preferred Qualifications

Experience with Apache Spark for large-scale data processing, including PySpark and Spark SQL. Knowledge of streaming technologies (Kafka, Kinesis, Flink) for real-time data pipelines.

Experience with dbt (data build tool) for SQL-based transformation workflows, including testing, documentation, and incremental processing. dbt has become the standard for analytics engineering, and experience with it is listed in over 40 percent of data engineering job postings [3].

Familiarity with modern data lakehouse architectures using table formats like Delta Lake, Apache Iceberg, or Apache Hudi, which combine the flexibility of data lakes with the ACID transactions of data warehouses.

Experience with data governance platforms (Alation, Collibra, Datahub) and data observability tools (Monte Carlo, Bigeye, Soda) signals a mature approach to data quality and reliability.

Tools and Technologies

Data engineers work across a layered data stack:

• Programming: Python (PySpark, Pandas, SQLAlchemy), SQL (the universal language of data), Java/Scala (for Spark and Kafka), Bash scripting
• Data Warehouses: Snowflake, Google BigQuery, Amazon Redshift, Databricks SQL Warehouse, Azure Synapse
• Data Lakes and Table Formats: AWS S3, Google Cloud Storage, Azure Data Lake Storage, Delta Lake, Apache Iceberg, Apache Hudi
• Processing Frameworks: Apache Spark, Apache Flink, dbt, Apache Beam
• Streaming: Apache Kafka, Amazon Kinesis, Google Pub/Sub, Confluent Cloud, Redis Streams
• Orchestration: Apache Airflow, Dagster, Prefect, Mage, AWS Step Functions
• Data Quality: Great Expectations, dbt tests, Monte Carlo, Soda, Bigeye
• Cloud Platforms: AWS (Glue, EMR, Redshift, S3, Lambda), GCP (Dataflow, Dataproc, BigQuery, GCS), Azure (Data Factory, Databricks, Synapse) [3]

Work Environment and Schedule

Data engineers work in office, hybrid, or fully remote settings. The role is highly remote-friendly because the work product is code and infrastructure configuration that can be developed, tested, and deployed from any location. The BLS reports that database administrators and architects held about 179,300 jobs in 2024, with concentrations in computer systems design, finance, insurance, and information services [1].

Standard work hours are 40 per week. On-call rotations are common — data pipelines that fail overnight can delay morning dashboards and reports that business leaders depend on. Typical on-call duties involve monitoring pipeline health, restarting failed jobs, investigating data quality alerts, and escalating infrastructure issues.

The work is intellectually challenging. Data engineers deal with messy source systems, inconsistent schemas, undocumented business logic, and scale challenges that require creative problem-solving. The best data engineers combine software engineering rigor with data domain expertise and a deep understanding of how analysts and scientists consume data.

Team structures vary. Data engineers may sit on a centralized data platform team, be embedded within product or analytics teams, or work in a hybrid model. Team sizes range from solo data engineers at smaller companies to data platform teams of 20 or more at large technology companies.

Salary Range and Benefits

The Bureau of Labor Statistics reports a median annual wage of $135,980 for database architects in May 2024, which is the closest BLS classification for data engineers [1]. The median for database administrators specifically was $104,620.

Data engineers at major technology companies earn significantly more. Total compensation (base + equity + bonus) for senior data engineers at companies like Meta, Google, and Netflix ranges from $200,000 to $400,000 depending on level and location [4].

The lowest 10 percent of database architects earned less than $81,000, while the highest 10 percent earned more than $200,280 [1]. Remote data engineering roles at distributed companies like Databricks, Snowflake, and dbt Labs offer competitive salaries regardless of location.

Benefits typically include comprehensive health insurance, 401(k) with employer match, education and certification budgets, conference attendance (Data Council, dbt Coalesce, Kafka Summit), remote work stipends, and equity compensation at technology companies.

Career Growth from This Role

Data engineers advance along technical or management tracks. The IC track progresses from Data Engineer to Senior Data Engineer (three to five years), Staff Data Engineer (six to ten years), and Principal Data Engineer. The management track moves from Data Engineering Lead to Data Platform Manager, Director of Data Engineering, VP of Data, and Chief Data Officer.

Specialization paths include analytics engineering (focusing on dbt-based transformation and analyst enablement), ML engineering (building feature stores and model serving infrastructure), streaming and real-time systems (Kafka, Flink expertise), data platform engineering (building internal data infrastructure products), and data governance and architecture (designing enterprise data strategy).

The analytics engineering specialization has emerged as a distinct career path, pioneered by the dbt community. Analytics engineers bridge data engineering and data analysis, writing SQL transformations that turn raw data into analyst-ready datasets [3].

Lateral transitions include moving into data science (adding modeling skills to existing data expertise), backend engineering (leveraging systems and database knowledge), solutions architecture (advising organizations on data platform selection), and product management for data tools (leveraging deep understanding of data practitioner needs).

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FAQ

What is the difference between a data engineer and a data scientist?

Data engineers build the infrastructure — pipelines, warehouses, and data models — that makes data available. Data scientists use that data to build statistical models, run experiments, and generate insights. Data engineers focus on reliability, scalability, and data quality; data scientists focus on analysis, prediction, and machine learning [2].

What programming languages do data engineers use?

SQL and Python dominate. SQL is used for data transformation, warehouse queries, and dbt models. Python is used for pipeline logic, Spark jobs, and scripting. Java and Scala are used in Spark and Kafka ecosystems. Bash scripting handles automation tasks.

Is a computer science degree required for data engineering?

A CS degree is preferred but not universally required. Data engineers also come from backgrounds in mathematics, statistics, physics, and self-taught programming. Strong SQL skills, Python proficiency, and demonstrable experience building data pipelines are more important than the specific degree.

What is the career outlook for data engineers?

Very strong. While the BLS projects modest 4 percent growth for the database architects category, private-sector data shows much higher demand growth driven by AI/ML initiatives, cloud migration, and data-driven decision-making. Data engineering consistently ranks among the most in-demand technical roles [1].

What does a typical day look like for a data engineer?

A typical day includes checking pipeline monitoring dashboards for overnight failures, fixing any broken or slow pipelines, attending standup with the data team, writing or reviewing pipeline code for two to four hours, meeting with data scientists or analysts about their data needs, and working on data model improvements or infrastructure upgrades.

Should I learn Snowflake, Databricks, or BigQuery?

Learn one deeply and understand the concepts well enough to switch. Snowflake and Databricks have the largest job markets. BigQuery is dominant in GCP environments. The SQL and data modeling skills transfer across all platforms.

What is analytics engineering and how does it relate to data engineering?

Analytics engineering is a specialization that emerged from the dbt community, focused on transforming raw data into analyst-ready datasets using SQL. It sits between traditional data engineering (building pipelines and infrastructure) and data analysis (creating reports and dashboards). Many data engineers evolve into analytics engineers or vice versa [3].

Citations:

[1] U.S. Bureau of Labor Statistics, "Database Administrators and Architects: Occupational Outlook Handbook," https://www.bls.gov/ooh/computer-and-information-technology/database-administrators.htm

[2] O*NET OnLine, "15-1243.00 - Database Architects," https://www.onetonline.org/link/summary/15-1243.00

[3] dbt Labs, "What is Analytics Engineering," https://www.getdbt.com/what-is-analytics-engineering

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

[5] Snowflake, "The Modern Data Stack," https://www.snowflake.com/guides/modern-data-stack

[6] Apache Airflow, "Apache Airflow Documentation," https://airflow.apache.org/docs/

[7] Built In, "Data Engineer Job Description," https://builtin.com/articles/data-engineer-job-description

[8] Robert Half, "2025 Technology Salary Guide," https://www.roberthalf.com/us/en/insights/salary-guide/technology