Senior Data Engineer

Key Skills: Databricks, Python, Java, Spark, SQL, AWS, Airflow, Kafka, Data Pipelines, ETL

Good to Have Skills: Experience enabling ML, AI, or GenAI workloads through strong data foundations. Exposure to modern BI and analytics tooling. Experience working in regulated or security-conscious environments. Consulting or client-facing delivery experience. PostgreSQL, Aurora, distributed processing frameworks, CI/CD, monitoring, and operational support of data platforms.

Roles & Responsibilities:

• Design, build, and operate reliable data pipelines using modern ETL/ELT approaches for enterprise clients.
• Engineer cloud-native data platforms, primarily on AWS with Azure exposure where required.
• Work with technologies such as Databricks, PostgreSQL, Aurora, and distributed processing frameworks.
• Ensure solutions are production-ready including security, testing, observability, and cost-efficiency requirements.
• Optimize data performance, reliability, and data quality in live production environments.
• Collaborate with analysts, data scientists, AI engineers, and platform teams on data solutions.
• Mentor junior engineers and contribute to shared standards and accelerators within the team.
• Contribute to technical design discussions and help improve system performance, scalability, and reliability.
• Support production systems by troubleshooting issues and implementing improvements as needed.

Experience Required: 10+ years of hands-on data engineering experience with strong production background. Proven track record making architectural and data modeling decisions in enterprise environments. Experience operating at significant scale and complexity with distributed systems and scalable data architectures