Data Engineer

• Design, implement, and optimize robust, scalable data pipelines and ETL workflows using modern Python tools and libraries.
• Build and maintain production-grade RESTful and/or GraphQL APIs to serve data to internal and external stakeholders.
• Collaborate with data analysts, scientists, and engineering teams to enable end-to-end data solutions.
• Containerize data services using Docker and manage deployments within Kubernetes environments.
• Develop and maintain CI/CD pipelines using GitHub Actions to automate testing, data validations, and deployment processes.
• Ensure code quality through rigorous unit testing, type annotations, and adherence to Python best practices.
• Participate in architecture reviews, design discussions, and code reviews in an agile development process.
• Proactively identify opportunities to optimize data access, transformation, and governance.

• Bachelors or Masters degree in Computer Science, Engineering, or a related technical field.
• 5+ years of hands-on experience in data engineering or backend development roles.
• Expert-level Python skills, with strong understanding of idiomatic patterns, async programming, and typing.
• Proven experience in building production-grade RESTful or GraphQL APIs using frameworks like FastAPI, Graphene, or Strawberry.
• Hands-on experience with Docker, container-based workflows, and CI/CD automation using GitHub Actions.
• Experience working with Kubernetes for orchestrating deployments in production environments.
• Proficient with SQL and data modeling; familiarity with ETL tools, data lakes, or warehousing concepts is a plus.
• Strong communicator with a proactive and self-driven approach to problem-solving and collaboration.

• Familiarity with data orchestration tools (e.g., Airflow, Prefect).
• Experience with streaming data platforms like Kafka or Spark.
• Knowledge of data governance, security, and observability best practices.
• Exposure to cloud platforms like AWS, GCP, or Azure.

Expertia AI Technologies