Senior Data Engineer (Python+AWS)

• Design, develop, and maintain scalable Python-based serverless applications and distributed systems
• Build and operate event-driven data pipelines, including ingestion, transformation, and processing of streaming data
• Develop and maintain robust data processing services leveraging AWS cloud-native services
• Design and implement data models and storage solutions across DynamoDB, S3, and analytical systems
• Ensure data quality through schema validation, data contracts, and testing strategies
• Implement and maintain automated testing (unit and integration)
• Develop and manage Infrastructure as Code using Terraform
• Contribute to CI/CD pipelines to ensure reliable and automated delivery
• Ensure system reliability through observability best practices (monitoring, logging, tracing)
• Troubleshoot production issues and implement alerting and resilience improvements
• Collaborate effectively in a distributed team, communicating progress, risks, and dependencies
• Take ownership of features end-to-end, from design through deployment and production support
• Use AI-assisted development tools responsibly to improve productivity while ensuring code quality and security compliance
• Maintain clear technical documentation and promote engineering best practices

• Mandatory

• 6+ years of experience designing, developing, deploying, and maintaining backend services and distributed systems in production using Python

• Strong experience with AWS serverless architecture, including AWS Lambda, Kinesis, DynamoDB, S3, Athena/Presto, and Redshift

• Hands-on experience with event-driven architectures, including streaming systems (e.g., Kinesis), SNS/SQS, and asynchronous processing patterns

• Strong Python engineering practices including OOP, typed code, Pydantic-based validation, and clean code principles

• Solid experience with automated testing using pytest and related tooling (pytest-mock, moto)

• Experience with Infrastructure as Code using Terraform
  Hands-on experience with CI/CD pipelines and Git-based workflows (e.g., Jenkins or similar)

• Strong understanding of data engineering concepts such as ETL/ELT, data lakes, data warehouses, schema validation, and data contracts

• Strong understanding of distributed systems concepts such as idempotency, retry handling, DLQs, and observability (logging, monitoring, tracing)

• Experience working with SQL and analytical query engines (e.g., Athena/Presto, Redshift)

• Familiarity with AWS IAM basics and secure cloud development practices
  Experience troubleshooting production systems, including alerting and monitoring (e.g., CloudWatch)

• Strong ownership and self-management in a fast-paced Agile environment, with the ability to independently deliver end-to-end solutions

• Ability to critically evaluate AI-generated outputs and apply AI tools responsibly in software development workflows

Nice to Have

• Experience with AWS Powertools

• Experience with OpenTelemetry, Grafana, Loki, Tempo, or Prometheus

• Experience with AWS Glue, Step Functions, or EventBridge

• Experience with dbt or Airflow

• Experience with Docker

• Experience with LLM APIs, AWS Bedrock, OpenAI API, RAG, embeddings, or vector databases

• Familiarity with modern Python tooling such as Ruff, Poetry/uv, and static type checkers (mypy/pyright)