Senior Software Engineer - Data Governance & Management Platforms
Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 3
API @ 4
Data Pipelines @ 4
Distributed Systems @ 4
Go @ 6
Java @ 6
Leadership @ 4
Machine Learning
Observability @ 4
Python @ 6
Scala @ 6
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Within Bloomberg's 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. The team works across data discovery, cataloging, classification, lineage, data quality, observability, reference and enrichment services, master data, common data, and usage data, with cross-cutting responsibilities for stewardship, metadata, semantics, privacy, controls, and architecture standards.
The role will serve as a technical lead and architect for the future-state Data Services platform. It requires strong computer science fundamentals, including algorithmic problem-solving, hands-on coding, and system design, together with deep experience in data governance and data management. The engineer will bridge data management requirements with high-performance software engineering patterns while driving architecture, setting standards, building prototypes, and guiding engineering teams across the department.
Responsibilities
- Define future-state architecture patterns and engineering standards for metadata capture, lineage, data quality, observability, data contracts, and semantic modeling.
- Transform data governance and data management capabilities into production-ready software patterns, reusable APIs, metadata flows, and platform workflows.
- Write clean, optimized code; construct prototypes; bootstrap critical modules; and validate technical implementations for complex platform updates.
- Partner across Data Governance Services, Data Integrity Services, Common Data Products, and Usage Data Products to connect services into a cohesive platform.
- Collaborate with engineering leads, product managers, data stewards, and data engineers to reduce duplication, enforce standards, and improve engineering quality.
- Evaluate modern enterprise data management tooling, assess build-versus-buy choices, and apply proven patterns from mature data organizations.
Requirements
- Deep proficiency in software architecture, problem-solving, and hands-on coding using languages such as Python, C++, Java, Scala, or Go.
- At least 7 years of industry experience in software engineering, data management, data governance, metadata platforms, or enterprise data architecture.
- Comprehensive understanding of data cataloging, metadata management, lineage tracking, data quality frameworks, observability, stewardship, and semantic layers.
- Experience architecting distributed systems, data pipelines, metadata integration workflows, and API-driven data platforms.
- Experience with mature enterprise data platforms such as DataHub, OpenMetadata, Collibra, Alation, Atlan, Purview, or custom internal equivalents.
- Ability to lead architectural discussions, communicate complex technical topics to diverse stakeholders, and mentor engineering teams.
- Preferred experience with semantic technologies, knowledge graphs, ontologies, and master or reference data management.
- Familiarity with AI/ML-driven data governance automation, including automated data classification, anomaly detection, metadata enrichment, and automated lineage inference.
- Leadership experience as a Manager, Tech Lead, Platform Architect, or Principal SME in an enterprise-scale data ecosystem.
Compensation and Benefits
The annual salary range is $160,000–$240,000 USD, plus benefits and bonus. Benefits may include merit increases, incentive compensation for exempt roles, paid holidays, paid time off, medical, dental, vision, short- and long-term disability benefits, 401(k) matching, life insurance, and wellness programs. Actual compensation may vary based on geographic location, work experience, market conditions, education or training, and skill level.