Data Engineer 4

Own design, development, and maintenance of advanced audience data ingestion pipelines including validation, schema enforcement, and loading into layered data storage (raw, foundation, master).

Optimize and scale end-to-end data flows across Snowflake transformations, Aerospike serving, and migration from legacy systems ensuring reliability, observability, and operational metrics.

Mentor junior engineers and influence future platform design focusing on workload isolation, partner onboarding automation, migration strategies, and reducing operational overhead.

7-10 years experience in data engineering with ownership of production data pipelines and platform-scale systems.

Strong expertise in SQL (incremental processing, CDC, merge logic), Python, Snowflake (Streams, Tasks, Dynamic Tables), and AWS ingestion patterns (S3-centric, event-driven).

Experience designing reliable batch and near-real-time ingestion pipelines handling structured and semi-structured data formats with schema validation and reject handling.

Bachelor's degree preferred; combination of coursework and professional experience also considered.

Senior data engineer with proven leadership in architecting complex ingestion-to-serving pipelines in migration-heavy, multi-system environments.

Experienced in multi-layered data platform design including Snowflake transformations and low-latency serving layers like Aerospike, with focus on operational scalability and observability.

Demonstrated ability to mentor engineers and influence cross-team engineering standards, platform simplification, and partner onboarding automation.