As a Data Engineer at Bjak, you will design, build, and maintain scalable data pipelines that power analytics, product insights, and downstream systems. You will collaborate with analytics, product, and engineering teams to ensure data is reliable, accessible, and production-ready at scale.
What You'll Do
- Design, build, and maintain scalable, fault-tolerant data pipelines (batch and/or streaming) for core business and product data.
- Ingest data from diverse sources including APIs, databases, event streams, and third-party services, ensuring high data quality and reliability.
- Design and manage data models and storage layers (data warehouses, data lakes) that support analytics and downstream use cases.
- Partner with analytics, product, and engineering teams to deliver clean, well-documented datasets that enable self-service analytics and experimentation.
- Implement monitoring, logging, and alerting to ensure pipeline reliability, performance, and cost efficiency.
- Enforce data governance best practices, including access control, privacy, documentation, and data lineage.
What We're Looking For
- 3+ years of experience as a Data Engineer, data-heavy Backend Engineer, or similar role.
- Strong programming skills in Python and/or TypeScript, with solid SQL proficiency.
- Good understanding of data modeling, ETL/ELT concepts, and analytics workflows.
- Hands-on experience with data warehouses (e.g. BigQuery, Snowflake).
- Experience building and operating production data pipelines.
Nice to Have
- Experience with cloud platforms (especially GCP).
- Exposure to streaming systems (Kafka, Pub/Sub).
- Familiarity with dbt and analytics engineering practices.
- Experience with data lake technologies.
- Exposure to ML data pipelines or feature stores.
Technical Stack
Python, TypeScript, SQL, BigQuery, Snowflake, GCP, Kafka, Pub/Sub, dbt, data lakes, ML data pipelines, feature stores
Team & Environment
Flat structure - execution and results matter more than titles
Benefits & Compensation
- Build and own data systems that operate at real scale
- Work on high-impact, business-critical data use cases
- High-ownership role with autonomy and trust
- Flat structure - execution and results matter more than titles
- Competitive compensation and strong growth opportunities
Compensation: Competitive compensation, Strong growth opportunities
