Requirements
- Over 8 years of professional experience in data engineering roles.
- Minimum of 5 years working directly with AWS data platforms and services.
- Advanced skills in Python or Scala for data processing and application development.
- Proven track record designing and managing large-scale ETL and ELT workflows.
- Deep knowledge of SQL and data modeling techniques, including OLAP, dimensional models, and lakehouse architectures.
- Extensive hands-on work with Spark or PySpark for distributed data processing.
- Direct experience using Airflow or equivalent workflow orchestration systems.
- Familiarity with developing and integrating REST APIs and microservices.
- Enterprise-level use of AI-powered coding assistants such as GitHub Copilot.
- Strong grasp of cloud security fundamentals, including IAM policies, encryption methods, networking, and compliance best practices.
Nice to Have
- Hold one or more AWS certifications, such as AWS Certified Solutions Architect or AWS Certified Data Analytics – Specialty.
- Background in real-time data streaming using tools like Kafka or Kinesis.
- Experience deploying and managing containerized workloads with Docker and Kubernetes.
- Working knowledge of modern data lake formats including Delta Lake, Apache Iceberg, or Apache Hudi.
- Prior involvement in setting up data observability frameworks to monitor data quality and pipeline health.
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