Access Control, Amazon Simple Storage Service (S3), Artificial Intelligence (AI), Cloud Computing, Computer Security, Continuous Deployment/Delivery, Continuous Integration, Database Extract Transform and Load (ETL), GitHub, Integration Testing, Object Oriented Programming (OOP), Python Programming/Scripting Language, Sales Pipeline, Simple Queue Service (SQS), Software Engineering, Test Automation, Test Plan/Schedule, Unit Test
Job Description:
We are not looking for typical ETL developers who just move data from point A to point B or build a unique pipeline for every single source. As we are building a generic, automated data platform where one single master job handles everything based on configuration parameters. Therefore, we need Software Engineers who happen to work with data. Any change these candidates make will impact the entire system, so they must be abstract, system-level thinkers.
Technical Profile Breakdown:
- Strong Core Python & Engineering Foundations: Writing clean, generic, reusable Python code using Object-Oriented Programming (OOP), classes, and functions. They must understand code architecture (e.g., how values/references are passed).
- Python Packaging & Deployment: Hands-on experience building Python packages, using wheel files, and publishing/serving them via PyPI.
- Automated Testing: Routine experience writing Unit Tests and Integration Tests in an automated CI/CD setup.
- CI/CD Tools: Strong familiarity with GitHub Actions for automating builds and deployments.
- PySpark Core Knowledge: They don't need to be a Databricks wizard, but they must know the engine under the hood. They need to understand:
- Optimizing PySpark code when things run slow.
- How partitioning and ordering work.
- System Security & Access Controls: Understanding basic security concepts, access-based controls, and how to securely control data access across systems.
Team Resource Requirements
- Databricks & Spark: Would be best if they have hands on expertise with both but ultimately core engine concepts (Spark) matter more than the platform wrapper.
- Cloud Ecosystems (AWS Redshift, Glue, S3): They don't need specific tool checklists, but they must understand the core underlying concepts like using cloud object/blob storage (S3) or data queues (SQS).
- AI Coding Tools: Exposure to using tools like GitHub Co-pilot to accelerate daily coding.