Job description
Key Responsibilities Design, build, and maintain scalable data pipelines (ETL/ELT) Develop and optimize data architecture, data lakes, and warehouses Ensure data quality, reliability, and integrity across systems Collaborate with Product, Engineering, and Analytics teams to define data needs Build real-time and batch data processing systems Optimize database performance and query efficiency Implement data governance, security, and best practices Mentor junior data engineers and promote engineering excellence 5+ years of experience in data engineering or related roles Strong proficiency in Python and SQL — not just writing queries, but designing reusable, tested pipeline code Hands-on AWS experience (required): Redshift, S3, Athena, ECS, EventBridge Experience building and maintaining data warehouses — Redshift experience is a strong plus Familiarity with workflow orchestration tools such as Airflow or equivalent, including ECS-based scheduling patterns Experience with multi-database environments: PostgreSQL/Aurora and MySQL/MariaDB Strong understanding of data modeling, dimensional design, and schema evolution Experience with streaming technologies such as Kafka is a plus Comfort working in a fast-moving product company where priorities shift and pipelines must be resilient Nice to Have Experience supporting machine learning pipelines Knowledge of data governance and privacy best practices Experience in fast-paced startups or product companies Exposure to BI tools (e.
g., Metabase, Tableau, Power BI)
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