Senior Data Engineer

Grade Level (for internal use): 11 Key Responsibilities Data Pipeline Transition and Platform Delivery Partner with the core Databricks team to support the transition of existing data pipelines to the target enterprise data platform.

About Spgi

S&P Global Dow Jones Indic e s At S&P Dow Jones Indices, we provide iconic and innovative index solutions backed by unparalleled expertise across the asset-class spectrum.

By bringing transparency to the global capital markets, we empower investors everywhere to make decisions with conviction.

We’re the largest global resource for index-based concepts, data and research, and home to iconic financial market indicators, such as the S&P 500 ® and the Dow Jones Industrial Average ® .

More assets are invested in products based upon our indices than any other index provider in the world.

With over USD 7.4 trillion in passively managed assets linked to our indices and over USD 11.3 trillion benchmarked to our indices, our solutions are widely considered indispensable in tracking market performance, evaluating portfolios and developing investment strategies.

S&P Dow Jones Indices is a division of S&P Global (NYSE: SPGI).

S&P Global is the world’s foremost provider of credit ratings, benchmarks, analytics and workflow solutions in the global capital, commodity and automotive markets.

With every one of our offerings, we help many of the world’s leading organizations navigate the economic landscape so they can plan for tomorrow, today.

For more information, visit www.spglobal.com/spdji .

What’s In It For You?

Advancing Essential Intelligence.

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.

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.

We take care of you, so you can take care of business.

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.

Responsibilities

• Provide hands-on technical guidance for enhancing, stabilizing, and optimizing pipelines after transition, with a focus on performance, scalability, maintainability, operational reliability, and secure AWS deployment patterns.
• Implement and promote repeatable engineering patterns for ingestion, transformation, testing, deployment, monitoring, and support across onboarded datasets.
• Ensure pipelines are designed and operated in a way that supports long-term platform consistency, reliability, observability, and ease of maintenance.
• Support the design and operation of cloud-native data pipelines using AWS services such as: Amazon S3 for durable data lake storage AWS Glue for data integration, cataloging, and processing AWS Lambda for event-driven processing
• Amazon Kinesis for streaming use cases AWS Lake Formation for governed data lake controls AWS IAM for secure access control
• Promote the use of encryption, environment-level controls, access policies, and platform guardrails to support secure access to platform resources and data products.
• Apply lakehouse technologies and concepts such as Delta Lake, Apache Iceberg, Databricks Unity Catalog, metadata-driven pipelines, and governed data access patterns.
• AI-Assisted Engineering and Context Engineering Use AI-assisted engineering tools such as Claude, GitHub CoPilot, LLMs, and similar platforms to accelerate data engineering delivery while maintaining enterprise quality and security standards.
• AI tooling to support common engineering activities, including: Code generation
• Code refactoring SQL and PySpark optimization
• Unit test generation
• Documentation creation Pipeline analysis
• Troubleshooting and debugging
• Data quality rule suggestions

Runbook and operational support documentation

• Apply prompt engineering and context engineering practices to improve the accuracy, relevance, and reliability of AI-generated outputs.
• Structure technical context for AI-assisted work, including requirements, schemas, data contracts, architecture constraints, coding standards, logs, error messages, acceptance criteria, and governance requirements.
• Create or contribute to reusable prompt templates, context packs, and engineering playbooks for common data platform activities such as Databricks migration, AWS data engineering, pipeline troubleshooting, and test generation.
• Review, validate, and refine AI-generated code, recommendations, documentation, and analysis before applying them to production-grade engineering work.
• Ensure responsible use of AI tooling by following enterprise standards for security, privacy, sensitive data protection, compliance, and human review.
• Data Onboarding and Asset-Agnostic Enablement Support asset-agnostic data onboarding patterns that can accommodate 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.
• Help offshore and global teams adopt common onboarding frameworks, technical standards, and delivery practices that improve speed, consistency, and reduce friction.
• Establish reusable ingestion and processing patterns across batch, streaming, and event-driven use cases.
• Create or contribute to reusable technical assets such as: Design patterns Reference implementations Onboarding templates Pipeline frameworks Technical documentation Operational runbooks AI-assisted engineering templates Support metadata-driven and configuration-driven pipeline patterns to improve scalability, consistency, and maintainability.
• 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 implement 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, enrichment, 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

• Work with business and platform stakeholders to support golden record / golden copy generation and maintenance for key financial datasets, including reference data, instruments, business entities, market data, investment data, risk data, and related mastered domains.
• Excellence Serve as a senior technical individual contributor for data platform engineering, with hands-on involvement across pipeline migration, lakehouse architecture, AWS-native data engineering, governance, AI-assisted engineering, and mastering integrations.
• Provide technical guidance to engineers, delivery partners, and platform stakeholders without direct people management responsibility.
• Contribute to architecture discussions, design reviews, code reviews, implementation planning, technical standards, and production readiness reviews.
• Translate architectural direction into practical engineering tasks, implementation patterns, and reusable technical assets.
• Version control
• Automated testing CI/CD Infrastructure as code
• Release automation Monitoring and alerting

Incident response Documentation Production support readiness

• Help establish cloud engineering standards for infrastructure automation, release management, and environment promotion using tools and services such as AWS CodePipeline, AWS CodeBuild, and infrastructure automation frameworks.
• Drive operational discipline across production data pipelines, including observability, logging, telemetry, support models, service ownership, and incident management.
• Collaboration, Governance, and Platform Standards Partner effectively with global platform, architecture, governance, security, Databricks-aligned, and data mastering teams to ensure offshore delivery aligns with enterprise standards.
• Act as a technical bridge between platform direction and engineering execution.
• Support governance requirements through appropriate controls around: Data lineage
• Schema consistency Data quality
• Metadata Retention Access control

Encryption Auditability Secure data distribution

• Ensure monitoring and operational health practices are in place using logging, alerting, telemetry, dashboards, and AWS-native operational tooling where appropriate.
• Help improve consistency and reusability across data onboarding and pipeline delivery by contributing to platform guardrails, standards, engineering playbooks, and reusable implementation patterns.

Requirements

• Qualifications Professional Experience 6–8+ 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, senior data engineer, lead data platform engineer, or equivalent technical role.
• Proven ability to support technical initiatives and influence engineering outcomes in a complex, execution-focused environment.
• Experience partnering with global or distributed teams to deliver platform and pipeline initiatives across time zones.
• Ability to provide technical guidance and improve delivery quality without direct people management authority.
• 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 AWS IAM AWS-native monitoring, logging, and security capabilities
• Working knowledge of technologies and concepts such as: Delta Lake Apache Iceberg Databricks Unity Catalog Metadata-driven pipelines
• Data lake governance

Lakehouse architecture Catalog-driven processing

• 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, and operational support for production data pipelines.
• Experience with AWS IAM, data lake governance, policy-based access controls, and secure platform operations.
• Familiarity with engineering best practices such as version control, automated testing, CI/CD, infrastructure automation, release management, and monitoring.
• Experience implementing data platform observability and reliability practices, including alerting, monitoring, telemetry, dashboards, and production support procedures.
• AI Tooling and LLM-Enabled Engineering Practical experience using AI tooling such as Claude, GitHub CoPilot, LLMs, or similar AI-assisted development platforms in an engineering context.
• Experience applying LLMs to data engineering or software engineering workflows, including code generation, refactoring, test creation, documentation, debugging, troubleshooting, and pipeline analysis.
• Understanding of prompt engineering and context engineering, including how to structure technical context, examples, constraints, schemas, logs, coding standards, and acceptance criteria to improve AI-generated outputs.
• Ability to use AI-assisted workflows to support Databricks development, AWS data engineering, SQL, PySpark, pipeline migration, data quality analysis, and operational support activities.
• Ability to evaluate, validate, and refine AI-generated code or recommendations before applying them to production-grade engineering work.
• Understanding of responsible AI usage in enterprise environments, including security, privacy, compliance, human review, and protection of sensitive or regulated information.
• Collaboration and Execution Strong communication and stakeholder management skills, with the ability to work effectively across offshore and global teams.
• Ability to translate architectural direction into actionable technical deliverables, implementation plans, and reusable engineering patterns.
• Comfortable operating in a role that blends hands-on engineering, technical guidance, and delivery enablement.
• Strong problem-solving skills and ability to navigate ambiguity in complex enterprise data platform environments.
• Ability to partner with engineers, architects, governance stakeholders, and business-aligned data teams to drive practical outcomes.

Preferred

• Qualifications Experience supporting enterprise data onboarding frameworks across multiple business domains, asset classes, or source system types.
• Familiarity with enterprise data cataloging, metadata management, business glossaries, lineage, operational metadata platforms, or semantic data layers.
• Knowledge of semantic modeling is a plus, particularly for supporting consistent business definitions, governed data consumption, data discoverability, and alignment between business terminology and technical data products.
• Exposure to semantic layers, canonical data models, domain models, metrics layers, business glossaries, ontologies, or knowledge graph concepts is a plus.
• Exposure to data mastering, MDM, reference data, market data, investment data, corporate actions, risk data, or golden record workflows in financial services environments.
• 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.
• 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.
• Familiarity with retrieval-augmented generation, embeddings, vector search, enterprise knowledge bases, or LLM-enabled documentation/search patterns is a plus.
• Experience in regulated or complex enterprise environments where data governance, data controls, auditability, lineage, privacy, and security requirements are important.
• Databricks certifications, AWS certifications such as AWS Certified Data Engineer or AWS Certified Solutions Architect, or equivalent practical platform experience are a plus.

Benefits

• We care about our people.
• That’s why we provide everything you—and your career—need to thrive at S&P Global.
• Health & Wellness: Health care coverage designed for the mind and body.
• Generous time off helps keep you energized for your time on.
• Access a wealth of resources to grow your career and learn valuable new skills.
• Secure your financial future through competitive pay, retirement planning, a continuing education program with a company-matched student loan contribution, and financial wellness programs.
• 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.
• 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.

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Pay Transparency

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