This job is with S&P; Global, an inclusive employer and a member of myGwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly.

About the Role:

Grade Level (for internal use):

12

Key Responsibilities

Data Pipeline Transition and Platform Delivery

- Partner with the core Databricks team to plan and execute the transition of existing data pipelines to the target data platform.

- Drive the implementation of repeatable engineering patterns for ingestion, transformation, testing, deployment, and monitoring across onboarded datasets.

- Ensure pipelines are designed and managed in a way that supports long-term platform consistency, reliability, observability, and ease of support.

- Guide the design and operation of cloud-native data pipelines using AWS services such as:

- Amazon S3 for durable data lake storage

- AWS Glue for integration, cataloging, and processing

- AWS Lambda for event-driven processing

- Amazon Kinesis for streaming use cases

- AWS Lake Formation for governed data lake controls

- Promote the use of AWS IAM, encryption, environment-level controls, and platform guardrails to enforce secure access to platform resources and data products.

- Support practical application of lakehouse technologies and concepts such as Delta Lake, Apache Iceberg, Databricks Unity Catalog, metadata-driven pipelines, and governed data access patterns.

Data Onboarding and Asset-Agnostic Enablement

- Define and operationalize onboarding patterns that support a broad range of data assets, domains,



and source systems without requiring bespoke platform redesign for each use case.

- Work with platform, data engineering, architecture, governance, and business-aligned teams to simplify and standardize how data is ingested, transformed, governed, and published to the enterprise platform.

- Create or contribute to reusable technical assets such as design patterns, reference implementations, onboarding templates, pipeline frameworks, technical documentation, and operational runbooks.

- Support asset-agnostic onboarding by ensuring data pipelines are configurable, metadata-driven, scalable, and aligned with enterprise data platform standards.

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.

- Help establish data quality controls, reconciliation processes, metadata alignment, and stewardship workflows required to support trusted mastered data in the platform.

- Contribute to issue resolution and continuous improvement related to mastering-related ingestion and distribution workflows.





- Support data mastering capabilities aligned with platforms such as NeoXam DataHub, including: Data acquisition, Cleansing, Enrichment, Mastering, Reconciliation, Golden copy generation & Downstream distribution of trusted data products.

Technical Leadership and Engineering Excellence

- Serve as a senior technical individual contributor for data platform engineering, providing expertise across pipeline migration, lakehouse architecture, AWS-native data engineering, governance, and mastering integrations.

- Influence technical direction without direct people management responsibility.

- Contribute to architecture discussions, design reviews, implementation planning, code reviews, technical standards, and production readiness reviews.

- Translate broader architectural direction into actionable engineering patterns, implementation plans, and technical deliverables.

- Promote engineering best practices including: Version control, Automated testing, CI/CD, Release automation, Monitoring and alerting, Incident response, Documentation.

- Help establish cloud engineering standards for infrastructure automation, release management, and workplace promotion using tools and services such as AWS CodePipeline, AWS CodeBuild, and infrastructure automation frameworks.

- Drive operational rigor across production data pipelines, including observability, logging, telemetry, support models, service ownership, and incident management.

Collaboration, Governance, and Platform Standards

- Partner effectively with global

📌 Associate Director, Data Platforms — Technical Lead (Hyderabad)
🏢 S&P Global
📍 Hyderabad