Job Summary (List Format):

- Lead the design, development, and operation of a scalable, cloud-native enterprise data platform on AWS.
- Ensure secure and reliable ingestion and processing of structured and unstructured data for analytics and downstream use on Databricks.
- Architect and implement AWS-based enterprise data lake solutions, setting standards for data ingestion, transformation, storage, and access.
- Develop and manage real-time and batch ingestion pipelines for internal systems and external data sources using various AWS services (AppFlow, Lambda, Glue, S3, Athena).
- Build event-driven data pipelines supporting real-time analytics use cases such as trading, operations, compliance, and surveillance.
- Implement strong security, governance, and compliance controls using AWS KMS, Secrets Manager, Security Hub, Config, and CloudTrail.
- Establish monitoring and alerting frameworks using AWS CloudWatch and Grafana to track pipeline and infrastructure health.
- Manage DevOps processes,



including CI/CD pipelines with GitLab, and automate data pipeline testing and deployment.
- Enforce data quality through validation, reconciliation, and monitoring frameworks; manage metadata and data lineage using AWS Glue Data Catalog.
- Provide hands-on technical leadership, mentor team members, participate in architecture discussions, and collaborate with analytics and business teams in an Agile environment.
- Apply strong programming skills in Python, PySpark, SQL, and expertise in AWS and Databricks.
- Demonstrate strong problem-solving abilities, ownership, and the capacity to lead in quick-paced Agile settings.
- Possess 7–10 years of experience in data/platform engineering, with proven work in cloud-native data platforms and both batch and real-time data processing; experience in financial services or wealth management is preferred.

📌 Data Engineering Lead (India)
🏢 Sparix Global
📍 India