Develop and optimize ETL pipelines from various data sources using Databricks on cloud (AWS, Azure, etc.)
Experienced in implementing standardized pipelines with automated testing, Airflow scheduling, Azure DevOps for CI/CD, Terraform for infrastructure as code, and Splunk for monitoring
Continuously improve systems through performance enhancements and cost reductions in compute and storage
Data Processing and API Integration: Utilize Spark Structured Streaming for real-time data processing and integrate data outputs with REST APIs
Lead Data Engineering Projects to manage and implement data-driven communication systems
Experienced with Scrum and Agile Methodologies to coordinate global delivery teams, run scrum ceremonies, manage backlog items, and handle escalations
Integrate data across different systems and platforms
Strong verbal and written communication skills to manage client discussions
Candidate Profile:
8+ years' experience in developing and implementing ETL pipelines from various data sources using Databricks on cloud
Experience in insurance domain/ data is must
Programming Languages SQL, Python
Technologies - IaaS (AWS or Azure or GCP), Databricks platform, Delta Lake storage, Spark (PySpark, Spark SQL).
Good to have - Airflow, Splunk, Kubernetes, Power BI, Git, Azure DevOps
Project Management using Agile, Scrum
B.S. Degree in a data-centric field (Mathematics, Economics, Computer Science, Engineering or other science field), Information Systems, Information Processing or engineering.
Excellent communication & leadership skills, with the ability to lead and motivate team members
Ability to work independently with some level of ambiguity and juggle multiple demands