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S&P Global

Delivers credit ratings, market data, indices

Data Engineer - Data Platforms

Data EngineeringPosted on Nov 10
Full-timeNo salary listedIn-person

About the Job

Requirements

  • Five to seven or more years of experience in data engineering, data platforms, or related technical domains.
  • Hands-on experience building and operating batch and/or streaming data pipelines.
  • Experience working with Databricks-based platforms and lakehouse architectures.
  • Strong familiarity with AWS data engineering services, including Amazon S3, AWS Glue, AWS Lambda, AWS Lake Formation, Amazon Kinesis, and AWS IAM.
  • Working knowledge of Delta Lake, Apache Iceberg, and Databricks Unity Catalog or equivalent governed lakehouse concepts.
  • Solid understanding of data quality, schema management, lineage and metadata basics, access control, encryption, and operational support.
  • Ability to provide technical guidance without people-management responsibility.
  • Experience collaborating with global teams across time zones and working through ambiguous delivery problems.
  • Ability to translate technical requirements into implementable tasks.
  • Practical experience using AI-assisted engineering tools such as Claude, GitHub Copilot, or LLM-based development features.
  • Ability to use AI effectively with basic context engineering practices, including inputs, constraints, examples, and acceptance criteria.
  • Ability to validate AI-generated outputs and follow responsible, secure usage practices.

Responsibilities

  • Partner with Databricks and platform teams to transition existing pipelines into the target enterprise data platform.
  • Provide hands-on engineering support to stabilize and optimize pipelines after transition, focusing on performance, scalability, maintainability, and operational reliability.
  • Implement and follow repeatable engineering patterns for ingestion, transformation, testing, deployment, and monitoring.
  • Support secure AWS deployment patterns and governed data access using AWS IAM, encryption, and platform guardrails.
  • Work with lakehouse technologies such as Delta Lake, Apache Iceberg, and Databricks Unity Catalog, and support metadata-driven pipeline design.
  • Assist with establishing and maintaining observability practices, including logging, telemetry, alerts, and dashboards, for production pipelines.
  • Support asset-agnostic onboarding by applying standard ingestion and transformation frameworks to new datasets.
  • Help offshore teams adopt common onboarding standards, delivery practices, and technical templates.
  • Contribute reusable technical assets such as reference implementations, onboarding templates, documentation, and operational runbooks.
  • Support batch and streaming onboarding patterns using AWS services such as AWS Glue, AWS Lambda, and Amazon Kinesis, or event-driven integrations as appropriate.
  • Support integration of platform datasets with the enterprise data mastering platform.
  • Help establish and maintain data quality controls, reconciliation, and metadata alignment for trusted mastered data.
  • Support golden record or golden copy generation and maintenance activities for key financial datasets, as applicable to the program.
  • Provide technical guidance to engineering peers through code reviews, design reviews, and implementation planning.
  • Contribute to engineering standards and delivery rigor across CI/CD, infrastructure automation, testing practices, and operational readiness.
  • Help ensure documentation quality and operational discipline through runbooks, support procedures, and incident learnings.
  • Act as a technical bridge between offshore execution teams and global platform stakeholders to resolve delivery blockers and maintain alignment.
  • Use AI tools such as Claude, GitHub Copilot, and LLMs to accelerate engineering tasks, including drafting code, refactoring, writing documentation, generating unit tests, and troubleshooting.
  • Apply basic prompt engineering and context engineering practices by providing technical background, constraints, schemas, examples, and acceptance criteria to improve output quality.
  • Validate and verify AI-generated outputs before adoption, especially for correctness, security, and maintainability.
  • Follow responsible AI usage expectations, including protection of sensitive data, credentials, and regulated information.

Desired Qualifications

  • Experience with enterprise onboarding frameworks, reusable ingestion patterns, and pipeline templates.
  • Familiarity with data cataloging and metadata management and governed data product concepts.
  • Knowledge of semantic modeling, such as semantic layers, business definitions, and canonical models.
  • Exposure to enterprise data mastering or MDM workflows, golden record concepts, and trusted data distribution.
  • Experience in regulated environments with strong governance, auditability, and security expectations.
  • Databricks and/or AWS certifications, or equivalent practical experience.

About the Company

S&P Global

Delivers credit ratings, market data, indices

S&P Global provides financial information and analytics for investors, corporations, and governments, including credit ratings, market data, and indices. It compiles large financial data, conducts research, and offers analytics, ratings, and benchmarks accessed via subscriptions, licenses, or transaction-based services. The company differentiates itself with an integrated suite that covers ratings, data, analytics, indices, and ESG solutions across global markets, backed by well-known brands. Its goal is to help clients make informed decisions, manage risk, and drive growth while prioritizing corporate responsibility.

Company size
10,001+
Company stage
IPO
Headquarters
New York City, New York
Founded
1917

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