Design and operate batch and streaming data pipelines.
Architect and evolve data storage layers (lake, analytical, operational).
Develop scalable data models for analytics, ML, and operational systems.
Ensure data quality, consistency, and observability across pipelines.
Implement real-time processing using event-driven architectures.
Build ingestion and transformation workflows (ETL/ELT).
Own backfills, reprocessing strategies, and data migrations.
Implement CI/CD, monitoring, and SLA-driven operational practices.
Partner with engineers, analysts, and data scientists to design reliable and scalable data solutions.
Contribute to data platform standards and best practices.

5+ years of experience building and operating production-grade data systems under SLA constraints.
Strong proficiency in SQL and Python.
Hands-on experience with streaming systems (Kafka or similar).
Experience with batch processing and workflow orchestration.
Production experience with relational databases (PostgreSQL or similar), including performance tuning, indexing, and partitioning.
Experience designing high-throughput and low-latency systems.
Solid understanding of point-in-time correctness and reproducible data pipelines.
Experience with caching systems (Redis or similar).
Cloud experience (AWS preferred).
Experience with analytical engines (Athena, StarRocks, ClickHouse, or similar).
Experience with distributed processing frameworks (Spark, Flink, Beam, or similar).
Experience implementing data quality frameworks and CI/CD for data pipelines.
Familiarity with vector databases or columnar storage systems.

Senior Data Engineer

Key skills