Data Engineer

Position Summary

You build and own the data infrastructure that analytics and ML teams depend on. At the Middle level, you build and extend ETL/ELT pipelines within an existing warehouse architecture. At the Senior level, you also design the warehouse's data model from scratch, set data-quality and governance standards, and are the person other engineers escalate a broken pipeline to at 2am.

Key Responsibilities

• Design and build scalable ETL/ELT pipelines using Python, Apache Spark, and Airflow.
• Manage and optimize cloud data warehouses (Snowflake, BigQuery, or Redshift) for query performance and cost.
• Implement data modeling solutions (Star/Snowflake schema) that hold up as the business adds new data sources.
• Own data quality, integrity, and governance - catch a broken pipeline before a stakeholder does, not after.
• Collaborate with Data Scientists and Analysts to prepare clean, well-documented datasets for ML models and dashboards.
• (Senior) Own the warehouse schema and pipeline architecture end-to-end, and set the data-quality bar for the team.

Qualifications

• English: fluent with good verbal and written communication - documents pipelines and data contracts so international stakeholders never have to guess.
• 3+ years (Middle) to 5+ years (Senior) of Data Engineering experience.
• Strong proficiency in Python and advanced SQL, including query optimization.
• Experience with big data tools (Spark, Kafka) and orchestration (Airflow/Prefect).
• Familiarity with at least one cloud data platform (AWS Glue, EMR, or Azure Data Factory).
• Understanding of CI/CD and testing practices applied to data pipelines, not just application code.
• AI-assisted engineering as daily practice: uses AI coding assistants for pipeline code, and applies AI-assisted anomaly detection to catch data-quality issues faster.
• (Senior) Track record of designing a data warehouse or pipeline architecture from scratch, and setting governance standards for a team.

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