Senior Data Management Professional - Analytics Engineer - DMO BI

Details

Bloomberg's Data Management Operations (DMO) Business Intelligence team supports data management excellence by enabling data teams and stakeholders to make informed, data-driven decisions. The team develops scalable analytics, reporting, and data products that support client-facing workflows and advances analytical capabilities from descriptive reporting toward diagnostic and predictive insights.

This hands-on, data-focused role centers on building, modeling, and optimizing analytical datasets. The successful candidate will own and evolve Foundational Reporting Datasets (FRDs), ensuring they are accurate, performant, reusable, and aligned with domain realities. The role involves translating complex financial data into stable analytical schemas while balancing immediate analytical needs with long-term scalability and governance.

Responsibilities

• Build, maintain, and evolve Foundational Reporting Datasets (FRDs) as the analytical backbone for reporting and analysis.
• Write and optimize SQL queries to clean, shape, and model noisy, real-world data into performant, reusable datasets.
• Write modular, version-controlled SQL and PySpark.
• Implement CI/CD processes and automated data testing to ensure pipeline reliability.
• Design storage layouts, partitioning strategies, and high-concurrency serving patterns such as One Big Table (OBT) for BI consumers.
• Implement source validation, data profiling, and observability checks to ensure stakeholder trust in the data.
• Make pragmatic tradeoffs involving correctness, scope, performance, and documentation, while reasoning about data semantics and grain.
• Work with domain experts and stakeholders to understand how data is produced, interpreted, and used.
• Develop domain knowledge and represent data accurately in cross-functional discussions.
• Identify analytical work that should be formalized into reusable data products.
• Help define boundaries between foundational datasets and decision-specific, reusable data products.
• Partner with Product Managers to define roadmap feasibility.
• Work with Software Engineers on upstream tooling, architecture, performance, and modeling decisions while remaining focused on the data layer.

Requirements

• 4+ years of experience as a BI analyst, analytics engineer, or in a similar data-focused role.
• Proven ability to turn messy, ambiguous data into trusted analytical assets.
• Comfort working under ambiguity and improving systems incrementally.
• Strong collaboration skills and interest in learning a data domain deeply.
• Ability to translate semi-structured producer data into stable analytical schemas, including OLTP-to-OLAP transformations.
• Deep hands-on experience with SQL-based data modeling.
• Deep hands-on experience with PySpark and Pandas or Polars.
• Experience with analytical dataset design, performance and efficiency considerations, storage and layout optimization for analytical tables, schema evolution, source validation, and data profiling.

Preferred Qualifications

• Experience working with complex or regulated datasets.
• Experience using Bloomberg Terminal and Company Financials products.
• Exposure to data product or platform-style thinking.
• Experience partnering closely with domain experts or subject-matter experts.
• Experience driving adoption of new systems.
• Interest in data governance, ownership, and reuse patterns.
• Familiarity with modern table formats and distributed query engines at scale, such as Iceberg, Delta, Trino, and Spark, or equivalent technologies.
• Exposure to high-concurrency BI serving patterns such as One Big Table (OBT).

Compensation and Benefits

• Annual salary range: USD 110,000–190,000, plus benefits and bonus.
• Benefits may include merit increases, incentive compensation for exempt roles, paid holidays, paid time off, medical, dental and vision coverage, 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.