Senior Data Engineer (Applied ML / Analytics)

The Brattle Group•Boston, MA

30d•$105,000 - $120,000

About The Position

The Brattle Group, a privately held, global economics consulting firm, is looking for a Senior Data Engineer (Applied ML / Analytics) to join our Boston, MA office. The Senior Data Engineer (Applied ML / Analytics) at Brattle has a dual role, spanning both client-facing delivery and internal research and development. The role sits at the intersection of data engineering and data science, with data engineering as the primary discipline and applied data science informing analytical decisions. Senior Data Engineers translate client questions and messy data into workable analytical approaches and exercise judgment about when new tools or methods are needed. On client work, they own project definition and key implementation decisions. In research and development, they identify gaps in existing capabilities then design and test proof-of-concept solutions to address them. We are looking for a Senior Data Engineer (Applied ML / Analytics) with strong grounding in data science and applied machine learning who can evaluate and contrast modeling approaches, articulate trade-offs, and guide decisions across both classical methods and newer techniques. The role works directly with Brattle Principals, clients, and junior data engineers to explain technical decisions, structure workstreams, and transition prototypes into scaled solutions. This role emphasizes modeling judgment, feature engineering, and evaluation rather than routine pipeline construction, building on Brattle’s existing data engineering and generative AI work (e.g., Python-based ML, vector search, LLM-driven workflows).

Requirements

Bachelor’s Degree in Computer Science, Computer Engineering, or related field; advanced degree is a plus.
4–6 years of professional development experience, with a focus on data engineering or related fields.
Proven track record in delivering complex data engineering projects on time and within scope.
Demonstrated ability to research, pilot, and implement new technologies or methodologies.
Expert-level proficiency in Python for data processing, automation, and scripting
Deep familiarity with machine learning concepts (e.g., predictive modeling, classification, generative AI, vector search).
Solid experience with SQL and performance optimization (indexing, query tuning).
Experience with Azure (or comparable cloud platforms) for data storage, pipelines, and services.
Familiarity with modern data formats (e.g., CSV, Parquet, JSON) and associated transformations.
Hands-on experience with version control (Git) and CI/CD pipelines.
Ability to assess project requirements and select the most appropriate data engineering tools, and identify gaps or limitations where no suitable tool or approach exists.
Strong mentorship and coaching skills, including reviewing code and providing feedback to junior team members.
Able to translate complex technical concepts into actionable solutions.
Communicate complex limitations or risks proactively and with polish, ensuring stakeholders have confidence in proposed approaches.
Ability to manage multiple parallel workstreams in a fast-paced environment.
Flexible mindset to adapt to changing project priorities and client needs.

Nice To Haves

advanced degree is a plus.

Responsibilities

Define and lead technical workstreams for client and internal projects, including analytical approach, technology selection, scope definition, and estimation.
Triage and assess incoming client data from a wide range of sources and maturity levels, determining data readiness, required normalization, and appropriate analytical paths.
Exercise independent judgment in ambiguous situations, evaluating modeling approaches and technical trade-offs, and articulating clear recommendations to project teams, principals, and clients.
Bridge data engineering and data science by translating client questions and messy data into appropriate analytical methods, and determining when classical ML, generative AI, or simpler approaches are warranted.
Identify gaps in existing capabilities and research, prototype, and evaluate new tools or methods to address them.
Develop proof-of-concept solutions and structure handoff plans for junior engineers to implement and scale.
Review code submitted by engineers, providing technical feedback and guidance on design, modeling choices, and maintainability.
Write reproducible Python-based scripts for data analysis, unstructured text processing, and exploratory modeling as needed.
Contribute to internal training and documentation, sharing approaches, lessons learned, and recommended practices.
Coordinate with IT as needed on software licensing and deployment of new technologies.