Sr. Managing Consultant/Managing Consultant Data Engineer- Financial Services Operations & Technology at Berkeley Research Group

Job description

We Do Consulting Differently Preferred locations: New York, Chicago, Washington, D.C., Dallas, and Boston. Strong candidates in other locations may be considered case by case. About the Practice Berkeley Research Group is launching a new Financial Services Operations & Technology practice. We combine deep financial services expertise with AI-enabled delivery to improve operations across commercial and consumer lending, payments, and asset and wealth management. Help build a new practice within an established consulting firm. You will develop market-leading AI solutions, work with our AI Co-lab to frame solutions with vendor partners, collaborate with client teams, and build reusable digital assets. You will also contribute to recruiting, new offerings, thought leadership, and delivery improvement. The Role We are considering candidates at the Managing Consultant and Senior Managing Consultant levels. The final title and compensation will reflect the selected candidate’s relevant experience and the scope of responsibility. You will lead hands-on data engineering workstreams that underpin AI-enabled financial services solutions. Working closely with client teams, you will turn operational data and expert knowledge into trusted, AI-ready digital assets. You will guide engineers and collaborate with domain experts, AI specialists, and change and risk practitioners to deliver production-quality data foundations and practical implementation roadmaps. What You’ll Do Lead data engineering workstreams from discovery through production handover, managing technical dependencies, delivery risks, and implementation decisions with client business and technology teams. Design and build batch and streaming pipelines in Python and SQL to ingest, transform, and reconcile data from operational systems, APIs, files, and third-party sources. Develop ingestion processes for structured records and unstructured documents, including extraction, metadata capture, exception handling, and traceability to source material. Design warehouse and lakehouse data models that support operational reporting, analytics, and AI use cases while preserving business definitions and reconciliation controls. Implement data quality checks, lineage, access controls, and PII handling with client security, privacy, and risk teams so that sensitive information is used appropriately. Prepare retrieval-ready data through document parsing, chunking, metadata enrichment, embedding pipelines, and access-aware indexing in collaboration with AI engineers. Work in the AI Co-lab with domain experts and vendor partners to frame data requirements, compare implementation options, and validate reusable approaches against realistic financial services scenarios. Build automated tests, deployment pipelines, monitoring, and recovery procedures that make data products dependable and give client teams clear ownership and operating guidance. Mentor engineers and review code and designs while coordinating with AI, application, change, and risk colleagues to deliver integrated solutions and measure their operational impact. Capture domain knowledge in reusable schemas, pipeline components, and implementation guides, and contribute technical estimates, assumptions, and data dependencies to proposals and the next client pursuit Qualifications Required A minimum of 5 years of prior applicable work experience is required to be considered for the Managing Consultant level. To be considered at the Sr. Managing Consultant level, a minimum of 7 years of experience is required. Demonstrate senior, hands-on delivery of production data pipelines and data products, with equivalent practical experience recognized regardless of formal education or certification. Strong Python and SQL skills and can design, debug, optimize, and test ingestion and transformation processes across APIs, databases, files, and documents. Implemented warehouse or lakehouse models and can explain choices around schema design, storage, orchestration, incremental processing, and reconciliation using technologies such as Spark, dbt, or comparable tools. Applied data quality, lineage, least-privilege access, and sensitive-data controls in production and can translate business and risk requirements into testable engineering controls. Prepare structured and unstructured data for AI retrieval, including document parsing, chunking, metadata design, indexing, and preservation of source provenance and access permissions. Delivered maintainable data services using version control, automated testing, CI/CD, monitoring, and cloud infrastructure, and can diagnose reliability, performance, and cost issues. Lead a complex technical workstream, mentor engineers, and collaborate directly with client stakeholders to clarify requirements, communicate trade-offs, and contribute credible scope and effort estimates. Ability to travel to client sites as needed. Preferred Industry experience building at a leading bank, lender, or financial services institution building operational applications. Relevant financial services experience in consumer lending, commercial lending, or payments. Experience in mortgage, credit card, personal loan, or auto lending, including servicing, collections, and default, is valuable; adjacent banking or wealth and asset management experience is a plus. Built retrieval data foundations for production AI applications, including vector or hybrid search, document versioning, and evaluation of retrieval quality. Experience with change data capture, event-driven processing, or distributed data platforms across cloud and hybrid environments. Evaluated vendor data capabilities or convert domain expertise into reusable data assets, accelerators, and client implementation patterns. Experience implementing the Snowflake platform. Success means delivering trusted, reusable data assets that enable governed AI solutions, improve measurable client operating outcomes, and leave client teams able to maintain and extend what you build. Managing Consultant salary range: $158,000-180,000 Senior Managing Consultant salary range: $190,000 - $210,000 Job title and level will be determined based on qualifications and experience. Candidates must be able to verify their legal right to work in the United States without employer sponsorship now or in the future. About BRG BRG combines world-leading academic credentials with world-tested business expertise and purpose-built emerging technologies. Our culture centers on agility and connectivity which sets us apart and gets you ahead. At BRG, our professionals include specialist consultants, industry experts, renowned academics, and leading-edge data scientists. Together, they bring a diversity of real-world experience, data, and human and artificial intelligence, to economics, disputes, and investigations; corporate finance; and performance improvement services that address the most complex challenges facing organizations across the globe. Our unique structure nurtures the interdisciplinary relationships that give us the edge, laying the groundwork for more informed insights and more original, incisive thinking. When paired with our global reach and resources, our diverse perspectives and technical capabilities make us uniquely capable to address our clients’ challenges. We get results because we know how to apply our thinking to your world. At BRG, we don’t just show you what’s possible. We’re built to help you make it happen. BRG is proud to be an Equal Opportunity Employer. Our hiring practices provide equal opportunity for employment without regard to race, religion, color, sex, gender, national origin, age, United States military veteran status, ancestry, sexual orientation, marital status, family structure, medical condition including genetic characteristics or information, veteran status, or mental or physical disability so long as the essential functions of the job can be performed with or without reasonable accommodation, or any other protected category under federal, state, or local law.

Requirements

Must-haves

We Do Consulting Differently
We are considering candidates at the Managing Consultant and Senior Managing Consultant levels. The final title and compensation will reflect the selected candidate’s relevant experience and the scope of responsibility.
What You’ll Do
Lead data engineering workstreams from discovery through production handover, managing technical dependencies, delivery risks, and implementation decisions with client business and technology teams.
Design and build batch and streaming pipelines in Python and SQL to ingest, transform, and reconcile data from operational systems, APIs, files, and third-party sources.
Develop ingestion processes for structured records and unstructured documents, including extraction, metadata capture, exception handling, and traceability to source material.
Design warehouse and lakehouse data models that support operational reporting, analytics, and AI use cases while preserving business definitions and reconciliation controls.
Implement data quality checks, lineage, access controls, and PII handling with client security, privacy, and risk teams so that sensitive information is used appropriately.
Prepare retrieval-ready data through document parsing, chunking, metadata enrichment, embedding pipelines, and access-aware indexing in collaboration with AI engineers.
Work in the AI Co-lab with domain experts and vendor partners to frame data requirements, compare implementation options, and validate reusable approaches against realistic financial services scenarios.
Build automated tests, deployment pipelines, monitoring, and recovery procedures that make data products dependable and give client teams clear ownership and operating guidance.
Mentor engineers and review code and designs while coordinating with AI, application, change, and risk colleagues to deliver integrated solutions and measure their operational impact.
Capture domain knowledge in reusable schemas, pipeline components, and implementation guides, and contribute technical estimates, assumptions, and data dependencies to proposals and the next client pursuit
Demonstrate senior, hands-on delivery of production data pipelines and data products, with equivalent practical experience recognized regardless of formal education or certification.
Strong Python and SQL skills and can design, debug, optimize, and test ingestion and transformation processes across APIs, databases, files, and documents.

Nice-to-haves

Preferred locations: New York, Chicago, Washington, D.C., Dallas, and Boston. Strong candidates in other locations may be considered case by case.
Industry experience building at a leading bank, lender, or financial services institution building operational applications.
Built retrieval data foundations for production AI applications, including vector or hybrid search, document versioning, and evaluation of retrieval quality.
Experience with change data capture, event-driven processing, or distributed data platforms across cloud and hybrid environments.
Evaluated vendor data capabilities or convert domain expertise into reusable data assets, accelerators, and client implementation patterns.
Experience implementing the Snowflake platform.
Success means delivering trusted, reusable data assets that enable governed AI solutions, improve measurable client operating outcomes, and leave client teams able to maintain and extend what you build.
Job title and level will be determined based on qualifications and experience.
Candidates must be able to verify their legal right to work in the United States without employer sponsorship now or in the future.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythonsqlci/cd

Platform: workday
Effort: long