Role Summary :

To lead a vibrant data team that is creating & maintaining re-usable data pipelines using Databricks, PySpark & GCP. The role will involve managing the work end to end, interaction and co-ordination with multiple stakeholders including US counterparts & other vendor team members.

Essential Responsibilities :

- Lead the overall data engineering lifecycle including proof of concepts, architecture, design, development, test, deployment and maintenance of data pipeline and solutions.

- Establish and implement standardized guidelines for ETL and data architecture, ensuring consistency and efficiency.

- Guide team to create frameworks that guarantee data quality, handle errors effectively, provide audit controls, and offer reusable transformation structures.

- Lead team to apply a comprehensive, enterprise-wide approach to building data models and data management strategies, ensuring seamless integration across upstream and downstream applications.

- Transform business requirements into creative and practical data solutions that address constraints and optimize processes.

- Effectively communicate with business users, US team, architects, data analysts, and other teams to ensure data solutions meet their needs and align with overall objectives.

- Lead and mentor multiple data engineering teams to achieve project objectives & deliverables.

- Manage day to day operation and ensure adherence to process, scope, quality and timelines.

- Participate in review of requirements, architecture & design.

- Highlight risks and concerns, work with US managers to establish alignment, contingency and mitigation plans as required.

- Constantly looking to identify impediments early, actively working to resolve those impediments, and escalate when needed.

- Track delivery statuses, project progress and take ownership of critical incidents till completion.

- Take ownership and accountability of critical projects (for work executed in India).

- Publish regular status to leadership team highlighting risks & issues among other things.

- Other duties as assigned or requested - approximately 50% technical and 50% management.

The experience we are looking to add to our team :

Required :

- 10 to 14 years of overall experience with majority of them in Data engineering.

- Experience managing complex data projects and programs involving data engineering, ingestion, transformation, aggregation, modeling and storage.

- Experience in managing enterprise grade ETL/ELT solutions using Pyspark / GCP DataProc / Databricks etc.

- Experience building, mentoring, and managing high performing teams spread across multiple geographical locations.

- Strong customer focus, communication, collaboration and problem-solving skills.

- Excellent communication, confidence & attitude.

- Experience in big data technologies (HDFS, Spark, Pyspark, Databricks etc).

- Experience in leading a team and delivering complex projects involving creation and maintenance of data pipelines.

Good to have :

- Google Cloud knowledge / experience.

- Knowledge on tools like Iceberg, DBT, Starburst.

- Agile experience.

- Healthcare experience.