Senior Backend Engineer (Python, Data Engineering Focus)

Who You Are

You are a back-end engineer with deep Python expertise who thrives on processing and structuring large volumes of data from diverse sources. You understand how to ingest, transform, and analyze data efficiently, ensuring it's always available, scalable, and reliable. You enjoy solving complex problems, working with real-time and batch data pipelines, and optimizing database and API performance. You know that building a REST API is the real challenge that lies in the data layer, and that's where you shine.

What You'll Do

• Design, develop, and maintain high-performance backend data pipelines to process large-scale telemetry, diagnostics, and sensor data.
• Work with structured and unstructured data, handling real-time data ingestion, transformation, and storage.
• Optimize SQL and NoSQL database performance for high-throughput workloads.
• Collaborate with the DevOps team to build efficient ETL workflows and data lakes.
• Integrate external APIs, data streams, and message queues (Kafka, RabbitMQ, SQS).
• Implement logging, monitoring, and performance tuning for backend services.
• Occasionally build or enhance RESTful services. lets be real; data engineering comes first.

What You Need to Succeed

• 5+ years of Python experience, focusing on data-heavy backend development.
• Expertise in data processing frameworks (Pandas, Dask, PySpark, or similar).
• Strong SQL & NoSQL skills (PostgreSQL, MySQL, DynamoDB, or similar).
• Experience working with message queues (Kafka, RabbitMQ, SQS) for asynchronous processing.
• Become familiar with distributed computing principles and how to handle large-scale data workflows.
• Experience with ETL processes, data lakes, and API integrations for data retrieval.
• Hands-on experience with cloud platforms (AWS preferred, but Azure/GCP is fine).
• Comfortable working in a fast-paced Agile environment with evolving requirements.

Nice-to-Have Skills (But Not Required)

• Experience with containerization (Docker, Kubernetes, AWS ECS).
• Knowledge of machine learning workflows (if interested in AI/ML).
• Some familiarity with Java (for understanding legacy code).
• Exposure to FastAPI, Flask, or Django, though again, REST is the easy part.