The Role:

We are looking for a Senior Data Engineer to architect and scale the data foundations for one of our high-growth client products. The ideal candidate is a Python expert who treats data infrastructure as software, implementing CI/CD, unit testing, and observability into every layer of the modern data stack. You are a perfect candidate if you are growth-oriented, you love what you do, and you enjoy working on new ideas to develop exciting products.

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.