Looking For Senior Platform Engineer -Data Platform

Tech Mahindra; Hyderabad, Pune District; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Tech Mahindra in Hyderabad is seeking engineers to design and implement a cloud-native data platform. You will build reusable Python components, develop CI/CD pipelines, and define standards for build, release, and deployment across distributed services.

The role emphasizes AI-powered development tools, AWS-based data workloads, Databricks integration, and strong collaboration within large cross-functional teams.

Qualifications

• Good understanding of modern data engineering stacks, primarily AWS and Databricks.
• Knowledge 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.

Responsibilities

• Design solution architecture for distributed, cloud-native data platform capabilities focusing on scalability, reliability, data flow and service boundaries.
• Build infrastructure-as-code based on reusable, composable constructs.
• Develop CI/CD pipelines and deployment automation for customer environments.
• Develop reusable Python libraries and shared components.
• Build reference data pipelines to showcase platform capabilities and codify best practices.
• Enhance observability across pipelines and services including metrics, logs, tracing and data lineage.
• Establish and evolve engineering standards for build, test, branching, release and deployment.

Skills

Python

AWS

Databricks

CI/CD

Observability

IaC

Tools

AWS CDK

Jenkins

Git

Bash scripting

Airflow

Job description

• 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 fast-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