About the Role:

Grade Level (for internal use):

13

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 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 for durable storage

  • AWS Glue for integration and catalog-driven 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, and environment-level controls to enforce secure access to platform resources and data products in line with enterprise governance expectations.

• 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, to increase engineering velocity, code quality, and operational effectiveness.

• Apply LLM-based tools to support common data engineering activities such as:

  • Code generation

  • Code refactoring

  • SQL optimization

  • PySpark optimization, etc.

• 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.

• Create reusable context packs, prompt libraries, and AI-enabled engineering playbooks for common data platform tasks, including Databricks pipeline migration, ingestion framework development, test generation, code review support, and operational troubleshooting.

• Use LLMs to accelerate understanding and modernization of legacy pipelines, including dependency analysis, code explanation, transformation logic interpretation, metadata extraction, and refactoring recommendations.

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 without requiring bespoke platform redesign for each use case.

• Work with partner teams to simplify and standardize how data is ingested, transformed, governed, and published to the platform.

• Establish reusable ingestion and processing patterns across batch and streaming use cases using technologies such as AWS Glue, AWS Lambda, Amazon Kinesis, or event-driven integrations where appropriate.

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.

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.

• Contribute to defining common entities, attributes, relationships, hierarchies, metrics, and business terms across key data domains such as instruments, issuers, accounts, portfolios, risk, market data, reference data, and investment data.

• Help connect semantic modeling concepts with metadata management, business glossaries, data catalogs, lineage, and governed data products.

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.

• Provide technical guidance to engineers and partner teams without direct people management responsibility.

• Contribute to design reviews, code reviews, implementation planning, and technical problem-solving for complex data platform initiatives.

• Support delivery execution by translating broader architectural direction into practical implementation patterns, engineering tasks, reusable technical assets, and AI-enabled productivity accelerators.

• Drive rigor across sprint delivery, production readiness, support models, and incident management for business-critical data platforms.

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.

• Support governance requirements through appropriate controls around lineage, schema consistency, data quality, retention, access control, auditability, metadata, and secure data distribution.

• Help establish cloud engineering standards for infrastructure as code, release automation, and environment promotion using tools and services such as AWS CodePipeline, AWS CodeBuild, and infrastructure automation frameworks where appropriate.

• Act as a technical bridge between offshore execution teams and global platform stakeholders to ensure alignment, reduce ambiguity, and accelerate delivery.

• Help define standards for responsible AI use in engineering workflows, including review practices, sensitive data handling, secure prompt construction, validation requirements, and production deployment controls.

Required Qualifications

Technical Leadership and Delivery Experience

• 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.

• Experience partnering with global or onshore stakeholders to deliver platform and pipeline initiatives across time zones.

• Ability to translate broader architectural direction into actionable technical designs, implementation patterns, and delivery plans.

• Comfortable operating in a role that blends hands-on engineering, technical strategy, architecture influence, delivery enablement, and cross-team technical mentorship.

Data Platform and Cloud Engineering Expertise

• Strong hands-on experience with modern data engineering and pipeline development, including batch and/or streaming data workflows.

• Experience working with Databricks-based data platforms and supporting migration or transition of pipelines into a lakehouse-oriented architecture.

• Strong familiarity with AWS cloud-native data engineering, including services such as Amazon S3, AWS Glue, AWS Lambda, AWS Lake Formation, Amazon Kinesis, and related orchestration, security, and monitoring capabilities used to build scalable and governed data platforms.

• Working knowledge of technologies and concepts such as Delta Lake, Apache Iceberg, Databricks Unity Catalog, metadata-driven pipelines, data catalogs, and governed lakehouse architectures.

• Experience supporting data integration patterns involving mastering, MDM, reference data, market data, investment data, risk data, or trusted data distribution workflows.

• Solid understanding of data quality, schema management, lineage, metadata, access control, encryption, observability, and operational support for production data pipelines.

• Experience with AWS IAM, data lake governance, policy-based access controls, and metadata-driven controls to support secure, policy-aligned platform operations.

• Familiarity with engineering best practices such as version control, automated testing, CI/CD, infrastructure automation, release management, and monitoring.

• Experience designing or implementing data platform observability and reliability practices, including alerting, monitoring, telemetry, operational dashboards, and production support procedures.

AI Tooling and Context Engineering

• Practical experience using AI-assisted engineering tools such as Claude, GitHub CoPilot, LLMs, or similar AI-enabled development tools in software engineering or data engineering workflows.

• Experience applying LLMs to engineering use cases such as code generation, refactoring, test generation, documentation, debugging, pipeline analysis, SQL optimization, PySpark optimization, and production support.

• Demonstrated understanding of context engineering, including how to structure technical requirements, architectural constraints, data schemas, data contracts, examples, coding standards, logs, and acceptance criteria to improve AI-generated outputs.

• Ability to create reusable context packs, prompt templates, engineering playbooks, and documentation patterns that improve consistency and quality of AI-assisted engineering work.

• Ability to critically evaluate AI-generated code, recommendations, and documentation for correctness, security, performance, maintainability, and alignment with enterprise standards.

• Experience using AI-assisted workflows to improve engineering productivity while maintaining appropriate controls for production-grade systems.

Collaboration and Execution

• Strong communication and stakeholder engagement skills, with the ability to work effectively across offshore and global teams.

• Ability to influence technical direction without relying on formal people-management authority.

• Experience contributing to design reviews, architecture discussions, technical standards, implementation planning, and production readiness assessments.

Preferred Qualifications

• Familiarity with enterprise data cataloging, metadata management, business glossary, lineage, operational metadata platforms, or semantic data layers.

• Knowledge of NeoXam DataHub is a strong plus, particularly for firms using it to centralize financial data, manage the data lifecycle from acquisition to distribution, enable automated workflows, and deliver a single source of truth for mastered datasets.

• Experience with NeoXam-style mastering workflows, including consolidation, cleansing, enrichment, reconciliation, stewardship, golden copy creation, and trusted downstream distribution of mastered data.

• Knowledge of semantic modeling is a strong plus, particularly for designing consistent business definitions, improving data discoverability, supporting governed data consumption, and aligning business terminology with technical data products.

• Experience applying AI-assisted engineering practices to data pipeline migration, Databricks development, AWS data engineering, metadata analysis, data quality investigation, documentation automation, or production support.

• Databricks certifications, AWS certifications such as AWS Certified Data Engineer or AWS Certified Solutions Architect, or equivalent practical platform experience are a plus.

Grade: 13 {13+ years of experience}

Location: Hyderabad

Shift Time: 12 to 9 pm IST

Working Model: twice a week / 9 days a month work from office

What’s In It For You?

Our Mission:

Advancing Essential Intelligence.

Our People:

We're more than 35,000 strong worldwide—so we're able to understand nuances while having a broad perspective. Our team is driven by curiosity and a shared belief that Essential Intelligence can help build a more prosperous future for us all.From finding new ways to measure sustainability to analyzing energy transition across the supply chain to building workflow solutions that make it easy to tap into insight and apply it. We are changing the way people see things and empowering them to make an impact on the world we live in. We’re committed to a more equitable future and to helping our customers find new, sustainable ways of doing business. Join us and help create the critical insights that truly make a difference.

Our Values:

Integrity, Discovery, Partnership

Throughout our history, the world's leading organizations have relied on us for the Essential Intelligence they need to make confident decisions about the road ahead. We start with a foundation of integrity in all we do, bring a spirit of discovery to our work, and collaborate in close partnership with each other and our customers to achieve shared goals.

Benefits:

We take care of you, so you can take care of business. We care about our people. That’s why we provide everything you—and your career—need to thrive at S&P Global.

Our benefits include: 

• Health & Wellness: Health care coverage designed for the mind and body.

• Flexible Downtime: Generous time off helps keep you energized for your time on.

• Continuous Learning: Access a wealth of resources to grow your career and learn valuable new skills.

• Invest in Your Future: Secure your financial future through competitive pay, retirement planning, a continuing education program with a company-matched student loan contribution, and financial wellness programs.

• Family Friendly Perks: It’s not just about you. S&P Global has perks for your partners and little ones, too, with some best-in class benefits for families.

• Beyond the Basics: From retail discounts to referral incentive awards—small perks can make a big difference.

For more information on benefits by country visit: https://spgbenefits.com/benefit-summaries

Global Hiring and Opportunity at S&P Global:

At S&P Global, we are committed to fostering a connected and engaged workplace where all individuals have access to opportunities based on their skills, experience, and contributions. Our hiring practices emphasize fairness, transparency, and merit, ensuring that we attract and retain top talent. By valuing different perspectives and promoting a culture of respect and collaboration, we drive innovation and power global markets.

Recruitment Fraud Alert:

If you receive an email from a spglobalind.com domain or any other regionally based domains, it is a scam and should be reported to [email protected]. S&P Global never requires any candidate to pay money for job applications, interviews, offer letters, “pre-employment training” or for equipment/delivery of equipment. Stay informed and protect yourself from recruitment fraud by reviewing our guidelines, fraudulent domains, and how to report suspicious activity here.

20 - Professional (EEO-2 Job Categories-United States of America), IFTECH202.2 - Middle Professional Tier II (EEO Job Group), SWP Priority – Ratings - (Strategic Workforce Planning)