Senior Data Management Professional - Analytics Engineer - DMO BI

Details

Bloomberg runs on data. Our products are fueled by powerful information. We combine data and context to paint the whole picture for our clients, around the clock – from around the world. In Data, we are responsible for delivering this data, news, and analytics through innovative technology quickly and accurately. We apply problem-solving skills to identify workflow efficiencies and implement technology solutions to enhance our systems, products, and processes.

Team

The Data Management Operations (DMO) sits within the Data organization and supports Data’s pursuit of data management excellence through aligning industry best practices with Bloomberg's established expertise in financial market data. DMO empowers our data professionals to make their products “ready-to-use” through promoting increased data discoverability, accessibility, appraisability, interoperability, and analysis-readiness.

The Business Intelligence team sits alongside our Data Quality and Process teams and supports DMO’s mission by empowering data teams and stakeholders to make informed, data-driven decisions from a client perspective. As a member of the Business Intelligence team, you will play a critical role in transforming data into actionable insights that drive operational performance and strategic outcomes across the organization.

We partner closely with Data product teams and Engineering counterparts to deliver scalable analytics, reporting, and data products that support client-facing workflows. Primary themes in this role include developing and standardizing analytical frameworks, building high-impact dashboards and data assets, and enabling self-service analytics across the department. You will also contribute to advancing our analytical capabilities, moving beyond descriptive reporting toward more diagnostic and predictive insights.

Responsibilities

• Build, maintain, and evolve Foundational Reporting Datasets (FRDs) that serve 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, and implement CI/CD processes and automated data testing to ensure pipeline reliability.
• Design storage layouts, partitioning strategies, and high-concurrency serving patterns (like One Big Table) for BI consumers.
• Implement robust source validation, data profiling, and observability checks so stakeholders have absolute trust in the data.
• Make pragmatic tradeoffs around correctness, scope, performance, and documentation; reason about data semantics and grain.
• Work closely with domain experts, Product, and Engineering partners to translate complex, real-world financial data into stable analytical schemas that support scalable reporting and reuse.
• Actively build domain intuition over time — learn why the data behaves the way it does, not just how it’s structured.
• Identify analytical work that is worth formalizing into reusable data products and help define clear boundaries between foundational datasets and decision-specific, reusable data products.
• Partner with Product Managers and Software Engineers to influence roadmap feasibility, upstream tooling, and architecture decisions while remaining focused on the data layer itself.

Requirements

• 4+ years of experience as a BI analyst, analytics engineer, or similar data-focused role (years used as a guide).
• Proven ability to turn messy, ambiguous data into trusted analytical assets.
• Comfort working under ambiguity and improving things incrementally.
• Strong collaboration skills and interest in learning a data domain deeply.
• Ability to translate semi-structured producer data into stable analytical schemas (OLTP to OLAP).

Deep hands-on experience with:

• SQL-based data modeling (modular, version-controlled SQL).
• PySpark and Pandas/Polars.
• Analytical dataset design and designing for schema evolution.
• Performance and efficiency considerations, including storage/layout optimization for analytical tables.
• Source validation, data profiling, and observability.

Nice to have

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

Compensation & Benefits

Salary Range: 110,000 - 190,000 USD Annual + Benefits + Bonus

The referenced salary range is based on the Company's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level.

We offer a comprehensive benefits plan and a range of total rewards that may include merit increases, incentive compensation (exempt roles only), paid holidays, paid time off, medical, dental, vision, short and long term disability benefits, 401(k) + match, life insurance, and various wellness programs. The Company does not provide benefits directly to contingent workers/contractors and interns.