- Design solution architecture for distributed, cloud-native data platform capabilities reasoning about scalability, reliability, data flow and system boundaries across services.
- Design and build infrastructure-as-code based on reusable, composable constructs.
- Build and maintain CI/CD pipelines and deployment automation that customer teams use to provision and operate their environments.
- Develop reusable Python libraries and shared components consumed across the platform.
- Build reference data pipelines that showcase platform capabilities and codify best practices for our customers.
- Design and improve observability across distributed data pipelines and services metrics, logging, tracing and data lineage.
- Establish and evolve engineering standards build, test, branching, release and deployment best practices.
- Troubleshoot, monitor and continuously improve the reliability of the capabilities we ship.
- Python used to build cloud infrastructure-as-code and reusable platform libraries.
- Solution architecture for distributed systems service boundaries, data flow, scalability and resilience patterns.
- Infrastructure as Code such as AWS CDK constructs and reusable patterns.
- CI/CD with Jenkins, Git and Bash scripting.
- Testing discipline unit and integration testing as a first-class practice.
- Build, release and deployment best practices, including branching strategies such as GitFlow.




- AI-powered development tools such as GitHub Copilot and Claude Code as part of a modern engineering workflow.
- AWS for data-focused workloads including EMR, MWAA/Airflow, S3, Lambda, SQS, IAM and CloudWatch.
- Databricks including workspace configuration, jobs, notebooks and Unity Catalog.
- Nice to have: Snowflake and Microsoft Azure.
- Good understanding of modern data engineering stacks, primarily AWS and Databricks.
- Understanding of data modelling and batch/streaming processing paradigms.
- Familiarity with Lakehouse technologies such as Apache Iceberg.
- Pipeline orchestration with Apache Airflow.
- Working knowledge of Apache Spark and SQL.

Observability and distributed systems
- Experience designing or operating observability platforms metrics, logging, tracing and data lineage across distributed pipelines.
- Comfort working with event-driven and API-based architectures
- across multiple services.
- Strong team player who collaborates effectively within large, cross-functional teams.
- Critical thinking and sound engineering judgement.
- Eagerness and ability to learn quickly and adapt to a quick-changing environment.
- Ability to understand priorities, the wider context, and how individual work fits the bigger picture.
- Ownership and accountability.
- Efficient communication across large teams and diverse stakeholders.

Skills:Core stack Python, AWS, Databricks

Platform focus

IaC, CI/CD, observability

📌 Looking For Senior Platform Engineer -Data Platform (Hyderabad)
🏢 Tech Mahindra
📍 Hyderabad