Sr. Managing Consultant/Managing Consultant Data Engineer- Financial Services Operations & Technology

BRG•Chicago, IL

23h•$158,000 - $210,000

About The Position

Berkeley Research Group is launching a Financial Services Operations & Technology practice that combines financial services expertise with AI-enabled delivery to improve operations across commercial and consumer lending, payments, and asset and wealth management. The role offers an opportunity to help build the practice by developing AI solutions, working with BRG’s AI Co-lab and vendor partners, collaborating with client teams, and creating reusable digital assets. The successful candidate may be hired at either the Managing Consultant or Senior Managing Consultant level; title and compensation will depend on relevant experience and scope of responsibility. The role leads hands-on data engineering workstreams underpinning AI-enabled financial services solutions. Working with client teams, the consultant turns operational data and expert knowledge into trusted, AI-ready digital assets, guides engineers, and collaborates with domain experts, AI specialists, and change and risk practitioners to deliver production-quality data foundations and implementation roadmaps. The position also contributes to recruiting, new offerings, thought leadership, delivery improvement, and future client pursuits. Preferred locations are New York, Chicago, Washington, D.C., Dallas, and Boston. Strong candidates in other locations may be considered case by case. Travel to client sites may be required. Candidates must be able to verify their legal right to work in the United States without employer sponsorship now or in the future.

Requirements

At least 5 years of applicable work experience for the Managing Consultant level; at least 7 years for the Senior Managing Consultant level.
Senior, hands-on experience delivering production data pipelines and data products; equivalent practical experience is recognized regardless of formal education or certification.
Strong Python and SQL skills, including the ability to design, debug, optimize, and test ingestion and transformation processes across APIs, databases, files, and documents.
Experience implementing warehouse or lakehouse models and explaining schema design, storage, orchestration, incremental processing, and reconciliation choices using technologies such as Spark, dbt, or comparable tools.
Experience applying data quality, lineage, least-privilege access, and sensitive-data controls in production and translating business and risk requirements into testable engineering controls.
Experience preparing structured and unstructured data for AI retrieval, including document parsing, chunking, metadata design, indexing, and preservation of source provenance and access permissions.
Experience delivering maintainable data services using version control, automated testing, CI/CD, monitoring, and cloud infrastructure, and diagnosing reliability, performance, and cost issues.
Ability to lead a complex technical workstream, mentor engineers, collaborate directly with client stakeholders, clarify requirements, communicate trade-offs, and contribute credible scope and effort estimates.
Ability to travel to client sites as needed.
Ability to verify legal authorization to work in the United States without employer sponsorship now or in the future.

Nice To Haves

Industry experience building operational applications at a leading bank, lender, or financial services institution.
Relevant experience in consumer lending, commercial lending, or payments.
Experience in mortgage, credit card, personal loan, or auto lending, including servicing, collections, and default.
Adjacent banking, wealth management, or asset management experience.
Experience building retrieval data foundations for production AI applications, including vector or hybrid search, document versioning, and retrieval-quality evaluation.
Experience with change data capture, event-driven processing, or distributed data platforms across cloud and hybrid environments.
Experience evaluating vendor data capabilities or converting domain expertise into reusable data assets, accelerators, and client implementation patterns.
Experience implementing the Snowflake platform.

Responsibilities

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 for 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.
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 for dependable data products, and provide client teams with 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 operational impact.
Capture domain knowledge in reusable schemas, pipeline components, and implementation guides.
Contribute technical estimates, assumptions, and data dependencies to proposals and future client pursuits.
Contribute to recruiting, new offerings, thought leadership, and delivery improvement.