Description
We are seeking a Senior Data Engineer to join our team in Hyderabad, Telangana. You will partner with the core Databricks team to plan and execute the transition of existing data pipelines to the target enterprise data platform.
Key Responsibilities
- Partner with the core Databricks team to plan and execute the transition of existing data pipelines to the target enterprise data platform.
- Drive implementation of repeatable engineering patterns for ingestion, transformation, testing, deployment, and monitoring across onboarded datasets.
- Guide the design and operation of cloud-native data pipelines leveraging relevant AWS services such as Amazon S3, AWS Glue, AWS Lambda, Amazon Kinesis, and AWS Lake Formation.
- Promote the use of AWS IAM, encryption, and environment-level controls to enforce secure access to platform resources and data products.
- Influence architectural decisions related to Databricks, Delta Lake, Apache Iceberg, Unity Catalog, metadata-driven processing, and governed data lake design.
AI-Assisted Engineering and Context Engineering
- Champion the adoption of AI-powered development tools, including Claude, GitHub CoPilot, LLMs, and other AI-assisted engineering solutions.
- Apply LLM-based tools to support common data engineering activities such as code generation, code refactoring, SQL optimization, and PySpark optimization.
- Develop and implement context engineering practices that provide AI tools with the necessary technical background, architectural constraints, data schemas, coding standards, platform patterns, security requirements, and governance expectations.
Data Onboarding and Asset-Agnostic Enablement
- Provide technical direction for offshore teams supporting data onboarding to the enterprise platform in an asset-agnostic manner.
- Define and operationalize onboarding patterns that can support a broad range of data assets, domains, and source systems.
- Work with partner teams to simplify and standardize how data is ingested, transformed, governed, and published to the platform.
Data Mastering Platform Integration
- Support integration of platform pipelines and datasets with the enterprise data mastering platform.
- Collaborate with upstream and downstream stakeholders to ensure mastered data can be consumed reliably through standardized interfaces and governed data flows.
Semantic Modeling and Data Discoverability
- Support semantic modeling practices that help create consistent business definitions, reusable data concepts, and governed consumption patterns across the enterprise data platform.
- Partner with business, architecture, data governance, and platform teams to align technical data structures with business-friendly semantic definitions.
Technical Influence and Engineering Excellence
- Serve as a senior technical expert for offshore data platform engineering, providing guidance on architecture, design patterns, implementation quality, and production readiness.
- Influence architectural decisions across pipeline migration, lakehouse design, cloud-native data engineering, metadata-driven processing, AI-assisted engineering, and governed data platform operations.
Collaboration, Governance, and Platform Standards
- Partner effectively with global platform, architecture, governance, security, data mastering, and Databricks-aligned teams to ensure offshore delivery aligns with enterprise standards.
- Contribute to technical discussions related to pipeline architecture, onboarding frameworks, cloud platform controls, data governance, AI-assisted engineering practices, semantic modeling, and mastering integrations.
Required Qualifications
- 10+ years of experience in data engineering, data platforms, cloud data architecture, software engineering, or related engineering domains.
- Demonstrated experience operating as a senior Individual Contributor, technical lead, principal engineer, staff engineer, distinguished engineer, architect, or equivalent role in complex data platform environments.
- Proven ability to provide technical guidance, influence architectural decisions, and drive engineering best practices across distributed teams.