Principal Data Engineer (PySpark)
Summary
Principal Data Engineer builds and scales real-time and batch data pipelines using PySpark, Kafka, Flink, and cloud orchestration tools, while leading a team and mentoring engineers.
We are seeking a Principal Data Engineer with 5+ years of hands‑on experience and a strong background in real‑time and batch data processing, containerization, and cloud‑based data orchestration. This role is ideal for someone passionate about building robust, scalable, and efficient data pipelines, and who thrives in agile, collaborative environments.
Key Responsibilities
- Design, build, and maintain real‑time data pipelines using streaming frameworks such as Kafka, Apache Flink, and Spark Structured Streaming.
- Develop batch processing workflows with Apache Spark (PySpark)
- Orchestrate and schedule data workflows using orchestration frameworks such as Apache Airflow and Azure Data Factory
- Containerize applications using Docker, manage deployments with Helm, and run them on Kubernetes
- Implement modern storage solutions using open formats such as Parquet, Delta Lake, and Apache Iceberg
- Build high-performance analytics engines using tools like Trino or Presto
- Collaborate with DevOps to manage infrastructure with Terraform and integrate with CI/CD pipelines via Azure DevOps
- Ensure data quality and consistency using tools like Great Expectations
- Write modular, well‑tested, and maintainable Python and SQL code
- Develop an observability layer to monitor and optimize performance across data pipelines
- Lead and mentor a team of data engineers
- Participate in agile ceremonies and contribute to sprint planning and reviews
Required Skills & Experience
- Advanced Python programming with a strong focus on modular and testable code
- Strong knowledge of SQL and experience working with large-scale datasets
- Hands‑on experience with at least one major cloud platform (Azure preferred)
- Solid experience with real-time data processing (Kafka, Flink, or Spark Streaming)
- Expertise in Apache Spark (PySpark) for batch processing
- Experience implementing lakehouse architectures and working with columnar storage (e.g., ClickHouse)
- Proficient in using Azure Data Factory or Apache Airflow for data orchestration
- Experience in building APIs to expose large datasets
- Solid experience with Docker, Kubernetes, and Helm
- Familiarity with data lake open formats such as Parquet, Delta Lake, and Iceberg
- Basic experience with Terraform for infrastructure provisioning
- Practical experience with data quality frameworks (e.g., Great Expectations)
- Comfortable working in agile development teams
- Proven ability in debugging and performance tuning of streaming and batch data jobs
- Experience with AI‑driven tools (e.g., text-to-SQL) is a plus
- Bachelor’s degree in computer science or related discipline