Director, Software Engineering

S&P Global•Richmond, VA

3d•Remote

About The Position

The Director, Software Engineering partners with global platform, architecture, governance, security, data mastering, and Databricks-aligned teams to ensure offshore delivery aligns with enterprise standards. The role collaborates with partner teams, offshore teams, and upstream and downstream stakeholders to advance data platform initiatives and engineering excellence. The position leads the transition of existing data pipelines to the target enterprise data platform with the core Databricks team, establishing repeatable patterns for ingestion, transformation, testing, deployment, and monitoring across onboarded datasets. It champions AI-assisted engineering tools and practices, including reusable context packs, and provides technical direction to offshore teams establishing asset-agnostic onboarding patterns across data assets, domains, source systems, and batch and streaming use cases. The role also integrates pipelines and datasets with the enterprise data mastering platform; establishes data quality controls, reconciliation processes, and metadata alignment; and supports consistent business definitions, reusable data concepts, and governed data consumption. As a senior technical expert, the position influences architectural decisions, guides engineers and partner teams, and contributes to design and code reviews for complex data platform initiatives.

Requirements

Typically 10 or more years of experience in data engineering, data platforms, cloud data architecture, software engineering, or related engineering domains.
Demonstrated experience as a senior individual contributor, technical lead, principal engineer, staff engineer, distinguished engineer, architect, or equivalent in complex data platform environments.
Strong hands-on experience with modern data engineering and pipeline development, including batch and/or streaming data workflows.
Experience with Databricks-based data platforms and lakehouse-oriented architectures.
Practical experience with AI-assisted engineering tools such as Claude, GitHub CoPilot, LLMs, or similar.
Experience applying LLMs to engineering use cases such as code generation and optimization.
External candidates reaching the final interview stage must attend at least one in-person interview, ordinarily at their nearest S&P Global office, before an offer can proceed.

Nice To Haves

Familiarity with enterprise data cataloging, metadata management, business glossaries, lineage, operational metadata platforms, or semantic data layers.
Experience with NeoXam DataHub or NeoXam-style mastering workflows, including consolidation, cleansing, enrichment, and trusted downstream distribution of mastered data.
Knowledge of semantic modeling for consistent business definitions, improved data discoverability, and governed data consumption.
Strong leadership skills, including coaching, empowering others to maximize their performance, and building consensus across groups with competing priorities.

Responsibilities

Partner with the core Databricks team to plan and execute the transition of existing data pipelines to the target enterprise data platform.
Implement repeatable engineering patterns for ingestion, transformation, testing, deployment, and monitoring across onboarded datasets.
Lead adoption of AI-powered development tools such as Claude, GitHub CoPilot, and LLMs to improve engineering velocity, code quality, and operational effectiveness; develop context engineering practices and reusable context packs.
Guide offshore teams in defining and operationalizing asset-agnostic onboarding patterns across data assets, domains, source systems, and batch and streaming use cases.
Integrate platform pipelines and datasets with the enterprise data mastering platform, establishing data quality controls, reconciliation processes, and metadata alignment.
Support practices for consistent business definitions, reusable data concepts, and governed consumption in partnership with business, architecture, data governance, and platform teams.
Influence architectural decisions and provide technical guidance to engineers and partner teams on offshore data platform engineering.
Contribute to design and code reviews for complex data platform initiatives.