Data Engineer
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
Requirements
- 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)