Senior Software Engineer - Data Governance & Management Platforms

About the Team

Bloomberg runs on data. Within the Data Management & Analytics Engineering department, the Data Services team builds platforms, services, and data products that help the firm understand, govern, protect, improve, and use data at scale. Data Services spans Data Governance Services, Data Integrity Services, Common Data Products, and Usage Data Products. The team's capabilities include data discovery, cataloging, classification, lineage, data quality, observability, reference and enrichment services, master data, common data, and usage data with cross-cutting responsibilities across stewardship, metadata, semantics, privacy, controls, and architecture standards.

Role Overview

We are seeking a high-caliber Staff Software Engineer to serve as a technical lead and architect for our future-state Data Services platform. This role requires an engineer who possesses strong core computer science fundamentals—excelling in algorithmic problem-solving, hands-on coding, and system design—while bringing deep domain experience in data governance and data management.

In this role, you will bridge domain-specific data management requirements (cataloging, lineage, quality, observability, control frameworks) and high-performance software engineering patterns. You will operate with Tech Lead / Staff-level scope: driving architecture, setting standards, building prototypes, and guiding engineering teams across the department.

Key Responsibilities

• Drive Technical Leadership & Architecture: Define future-state architecture patterns and engineering standards for metadata capture, lineage, data quality, observability, data contracts, and semantic modeling.

• Translate Data Concepts to Scalable Code: Transform data governance and data management capabilities into production-ready software patterns, reusable APIs, metadata flows, and platform workflows.

• Hands-On Development & Prototyping: Actively write clean, optimized code, construct prototypes, bootstrap critical modules, and validate technical implementations to de-risk complex platform updates.

• Unify Data Services Capabilities: Partner across Data Governance Services, Data Integrity Services, Common Data Products, and Usage Data Products to connect disparate services into a seamless, cohesive platform.

• Provide Cross-Team Technical Enablement: Collaborate closely with engineering leads, product managers, data stewards, and data engineers to reduce duplication, enforce standards, and elevate engineering quality.

• Evaluate & Innovate: Stay current with modern enterprise data management tooling and industry trends; evaluate build vs. buy choices and import proven patterns from mature data organizations.

Minimum Qualifications

• Strong Software Engineering Fundamentals: Deep proficiency in software architecture, and problem-solving, with proven hands-on coding capabilities in languages such as Python, C++, Java, Scala, or Go.

• 7+ Years of Industry Experience: Track record in software engineering, data management, data governance, metadata platforms, or enterprise data architecture.

• Data Governance & Management Mastery: Comprehensive understanding of core data governance concepts including data cataloging, metadata management, lineage tracking, data quality frameworks, observability, stewardship, and semantic layers.

• Platform & System Design Expertise: Demonstrated success architecting distributed systems, data pipelines, metadata integration workflows, and API-driven data platforms.

• Hands-On Experience with Data Tools: Exposure to or experience implementing mature enterprise data platforms (e.g., DataHub, OpenMetadata, Collibra, Alation, Atlan, Purview) or custom internal equivalents.

• Technical Influence & Communication: Proven ability to lead architectural discussions, communicate complex technical topics to diverse stakeholders, and mentor engineering teams.

Preferred Qualifications

• Semantic Technologies & Knowledge Graphs: Experience with semantic technologies, knowledge graphs, ontologies, and master/reference data management.

• AI/ML Integration: Familiarity with AI/ML-driven data governance automation (e.g., automated data classification, anomaly detection, metadata enrichment, automated lineage inference).

• Leadership Track Record: Background serving as a Manager, Tech Lead, Platform Architect, or Principal SME in an enterprise-scale data ecosystem.