Data Engineer - Platform & Pipelines

Job Duties

We are implementing a strict Medallion Architecture to organize petabytes of industrial data. This role is for a Data Engineer who excels at transforming raw chaos into structured, queryable assets.

You will build and maintain the ELT pipelines that move data from "Bronze" (Raw) to "Silver" (Cleaned) and "Gold" (Aggregated). You will work with Delta Lake (On-prem/Databricks), Polars and Airflow to ensure data quality and availability for Data Scientists and the Knowledge Graph.

What You’ll Do

• Pipeline Development: Develop and maintain robust Airflow DAGs to orchestrate complex data transformations.
• Data Transformation: Use Spark (when scale requires) and Polars to clean, enrich, and aggregate data according to business logic.
• Architecture Implementation: Enforce the Medallion Architecture patterns, ensuring clear separation of concerns between data layers.
• Performance Tuning: Optimize processing workflows (Polars/Spark) jobs and SQL queries to reduce costs and execution time; make intelligent decisions on when to use Polars vs. Spark.
• Deployment & Operations: Manage code deployment to on-prem and cloud infrastructure, including containerization and environment configuration.
•  Data Quality: Implement comprehensive data validation checks and quality gates between medallion layers.
• Data Cataloging: Maintain the metadata and catalog entries to ensure all data assets are discoverable and documented.

The Technology Stack

• Orchestration: Apache Airflow.
• Data Processing: Polars (primary for ETL), PySpark/SQL (for massive scale)
• Compute: Single-node workers (Polars), Databricks/Spark clustrers (when scale requires)
• Storage: Delta Lake, Parquet, S3/Blob Storage, MinIO
• Language: Python 3.12+ (w/ Polars), SQL.

Qualifications

• The Structured Thinker: You love organizing data. You understand the importance of schemas, data typing, and normalization.
• Quality Obsessive: You don't just move data; you test it. You implement checks to ensure no bad data reaches the Gold layer.
• Pipeline Builder: You view data engineering as software engineering. You write modular, reusable code for your transformations.

Knowledge, Skills, and Abilities

Must Haves:

• 3+ years of experience in Data Engineering.
• Strong proficiency in Apache Airflow and Databricks.
• Experience implementing Medallion/Delta Lake architectures.
• Strong SQL and Python skills.
• Advanced English communication skills.

Good to Have:

• Experience with Unity Catalog or other governance tools.
• Familiarity with dbt (data build tool).
• Background in processing telemetry or sensor data.