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

Berkeley Research Group; New York (NY); On-site; USD 180,000 - 250,000; Full time

Job summary

Berkeley Research Group is launching a new Financial Services Operations & Technology practice. You will lead hands-on data engineering workstreams, turning operational data and domain knowledge into AI-ready digital assets.

You will mentor engineers, collaborate with AI specialists and client teams, and help deliver production-quality data foundations and implementation roadmaps. You will design batch/streaming pipelines, ingestion processes, and lakehouse models, while ensuring data quality,

Qualifications

• 5+ years of relevant work experience for Managing Consultant level; 7+ years for Sr. Managing Consultant.
• Hands-on delivery of production data pipelines and data products at senior level.
• Strong Python and SQL skills for ingestion, transformation, and orchestration.
• Experience with warehouse/lakehouse models and data governance controls.
• Ability to translate business and risk requirements into engineering tests and controls.
• Prepare data for AI retrieval with document parsing, indexing, and metadata design.
• Deliver maintainable data services with version control, CI/CD, monitoring, and cloud infra.
• Lead complex workstreams, mentor engineers, and communicate estimates to clients.

Responsibilities

• Lead data engineering workstreams from discovery through production handover.
• Design and build batch and streaming pipelines in Python and SQL.
• Develop ingestion for structured and unstructured data with metadata capture.
• Design warehouse/lakehouse models to support reporting and AI use cases.
• Implement data quality, lineage, access controls, and PII handling.
• Prepare retrieval-ready data with parsing, chunking, and indexing.
• Collaborate with AI Co-lab and vendor partners to frame data requirements.
• Build tests, deployment pipelines, monitoring, and recovery procedures.
• Mentor engineers and coordinate with AI, application, change, and risk teams.

Skills

Python

SQL

Data pipelines

Mentoring

Client collaboration

Leadership

Tools

Spark

dbt

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 *…*
• 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 finan