Data Engineer at Confluent: Roles, Interviews, and Compensation in 2026

DE at Confluent in 2026

Confluent was founded in 2014 by Jay Kreps, Neha Narkhede, and Jun Rao, the three engineers who created Apache Kafka at LinkedIn starting in 2010 and donated it to the Apache Software Foundation in 2011. Kafka is the default backbone for event streaming at most large companies, and Confluent is the steward and primary commercial vendor of that ecosystem.

Data engineers at Confluent split across three broad orgs. Engine engineering works on the Kafka broker, KRaft (the Raft-based controller that replaced ZooKeeper), the storage layer, and tiered storage. Stream processing and platform engineering covers Kafka Streams, ksqlDB, Connect, Schema Registry, and Flink, which Confluent added after acquiring Immerok in January 2023. Confluent Cloud engineering builds the managed service that runs on AWS, Azure, and GCP and now drives the majority of revenue.

Confluent has been public on NASDAQ under the ticker CFLT since the June 2021 IPO. That changes the equity calculus: grants now have a transparent vesting and liquidity path rather than the indefinite lockup typical of private hires. The DE org has grown alongside Confluent Cloud adoption and the rollout of Tableflow, the Apache Iceberg integration announced at Kafka Summit 2024 that materializes Kafka topics as queryable lakehouse tables.

Job titles you will see on careers.confluent.io include Software Engineer (Kafka), Senior Software Engineer (Stream Processing), Staff Software Engineer (Cloud), and Solutions Engineer - Data. Hub locations are Mountain View, Palo Alto, Austin, London, and Bengaluru, with remote allowed for many North America roles.

Interview process and bar

The Confluent DE interview loop runs five to six rounds and typically takes three to five weeks end to end. The structure is consistent across engine, stream-processing, and Cloud engineering, with the system-design round weighted heavily for senior candidates and a coding bar that leans toward distributed-systems primitives rather than algorithmic puzzles.

1. Recruiter screen - role fit, location, comp expectations, and a quick read on Kafka, streaming, and distributed-systems exposure.
2. Technical phone screen - one coding problem (Java is common given the Kafka codebase, but Python or Go is fine for most teams) plus a short discussion of Kafka concepts: partitioning, replication, consumer groups, and offset management.
3. Coding round - a medium problem with a concurrency or data-structure twist; expect to discuss correctness under failure, not just complexity.
4. Streaming and data modeling - design a topic layout and processing topology for a real scenario (clickstream aggregation, change-data-capture pipeline, fraud detection). Schema Registry, partition keys, and exactly-once semantics come up directly.
5. System design - design a streaming system at scale. Kafka internals, KRaft, tiered storage, multi-region replication with Cluster Linking, and Connect-based ingestion are fair game.
6. Hiring manager and values - ownership, customer obsession, and how you handle ambiguity on a fast-moving distributed system.

Bar-raisers look for depth on log-structured storage (segments, compaction, retention), the difference between at-least-once and exactly-once delivery and how Kafka achieves the latter through idempotent producers and transactions, and a clear mental model of when to reach for Kafka Streams versus ksqlDB versus Flink. Open-source contributions to Apache Kafka, Kafka Streams, or any of the Connect connectors are noticed at screening and shorten the path through early rounds.

Compensation by level

Compensation at Confluent is a balanced mix of base, equity, and bonus, and the June 2021 IPO means RSU grants vest into liquid CFLT shares on a transparent quarterly schedule. Levels.fyi data for US-based data engineers and software engineers at Confluent shows the following ranges as of early 2026.

Solutions Engineers and customer-facing DEs are paid on a comparable IC ladder with a smaller equity slice and a larger variable component tied to customer outcomes. European offers are typically 60-75 percent of US numbers in local currency, with London and Bengaluru being the most active non-US hiring hubs for DE roles.

Tech stack: Kafka core + Streams + ksqlDB + Connect + Tableflow + Schema Registry

Confluent DEs work on the same stack the company sells, which means most skills transfer directly to any organization that runs Kafka in production - a long list that now includes most of the Fortune 500.

• Apache Kafka - the core distributed log. Confluent employs many of the original committers and PMC members. Expect to know partitioning, replication, ISR (in-sync replicas), KRaft (the Raft-based controller that replaced ZooKeeper), and tiered storage that offloads older segments to object storage.
• Kafka Streams - the JVM library for building stream processing applications directly against Kafka topics. State stores, interactive queries, and exactly-once processing semantics are table stakes for the interview and the job.
• ksqlDB - the SQL-on-streams engine that Confluent open sourced in 2017 (then KSQL) and rebranded with embedded materialized views in 2019. DEs use it for streaming ETL, real-time dashboards, and joining streams to tables.
• Kafka Connect - the connector framework. The Confluent Hub lists hundreds of source and sink connectors. DEs write, certify, and operate connectors against databases, object stores, and SaaS systems.
• Schema Registry - the central store for Avro, JSON Schema, and Protobuf schemas with compatibility checking. Senior DEs design the schema-evolution strategy that keeps producers and consumers compatible as topics change.
• Tableflow - announced at Kafka Summit 2024, Tableflow materializes Kafka topics as Apache Iceberg tables that downstream lakehouse query engines (Spark, Trino, Snowflake, Databricks) can read directly. It collapses the streaming-to-analytics handoff that DEs historically wired up by hand with Connect plus a sink.
• Apache Flink - added to the Confluent stack after the January 2023 acquisition of Immerok and now offered as a managed service in Confluent Cloud. Used for complex stateful stream processing where Kafka Streams or ksqlDB are too constrained.
• Languages - Java is dominant in the Kafka and Streams codebases; Go and Python are common on Cloud and tooling teams; SQL is universal once ksqlDB and Flink SQL enter the picture.

Resumes that name specific Kafka and streaming features and quantify scale (messages per second, partitions managed, end-to-end latency, exactly-once topologies in production) read better than generic 'built real-time pipelines' bullets.