What we’re looking for:

• 5+ years of experience building complex data processing applications using Python (Pandas, PySpark, or Dask).
• Advanced SQL skills for complex transformations, window functions, and query optimization in cloud warehouses.
• Deep experience with dbt (data build tool) for managing the T in ELT, including documentation and testing.
• Proven experience with Apache Airflow, Prefect, or Dagster for managing complex dependency graphs.
• Hands-on experience with Snowflake, BigQuery, or AWS Redshift.
• Strong understanding of Dimensional Modeling (Star/Snowflake schema) and Data Vault 2.0.
• Experience with Git, Docker, and implementing CI/CD for data pipelines.

Nice-to-Have:

• Experience building Real-time Pipelines using Kafka or Flink.
• Familiarity with Data Contracts and Data Quality frameworks (Great Expectations, Monte Carlo).
• Knowledge of Vector Databases (Pinecone, Milvus) for AI/LLM applications.
• Infrastructure as Code (Terraform) experience.

Responsibilities:

• Build and maintain scalable, automated ELT/ETL pipelines that provide a “single source of truth” for the organization.
• Implement rigorous automated testing and monitoring to ensure data integrity and reliability.
•  Optimize warehouse storage and compute costs while reducing pipeline latency.
• Partner with Data Scientists and Product Managers to translate business requirements into technical data models.
• Promote a “DataOps” culture within the team, conducting code reviews and sharing best practices.

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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