Senior Managing Consultant Data Engineer

Berkeley Research Group, LLC is hiring a Senior Managing Consultant Data Engineer to lead hands-on data engineering workstreams for AI-enabled financial services solutions.

Responsibilities

• Lead data engineering workstreams from discovery through production handover, coordinating technical dependencies, delivery risks, and implementation decisions with client business and technology teams
• Design and build batch and streaming data pipelines using Python and SQL to ingest, transform, and reconcile data from operational systems, APIs, files, and third-party sources
• Develop ingestion processes for both structured records and unstructured documents, including extraction, metadata capture, exception handling, and traceability back to source material
• Create warehouse and lakehouse data models for operational reporting, analytics, and AI use cases, while preserving business definitions and reconciliation controls
• Implement data quality, lineage, access controls, and PII handling in partnership with client security, privacy, and risk teams
• Prepare retrieval-ready datasets for AI by working with AI engineers on document parsing, chunking, metadata enrichment, embedding pipelines, and access-aware indexing
• Collaborate in an 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 to support reliable data products and provide client teams clear ownership and operating guidance
• Mentor engineers and review designs and code 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, and contribute technical estimates, assumptions, and data dependencies to proposals and next client pursuits

Requirements

• Minimum 5 years of prior applicable work experience required for Managing Consultant level; 7 years required for Sr. Managing Consultant level
• Show senior, hands-on delivery of production data pipelines and data products; equivalent practical experience considered regardless of formal education or certification
• Strong Python and SQL skills, including ability to design, debug, optimize, and test ingestion and transformation processes across APIs, databases, files, and documents
• Hands-on experience implementing warehouse or lakehouse models; ability to explain decisions related to schema design, storage, orchestration, incremental processing, and reconciliation using tools such as Spark, dbt, or comparable options
• Production experience applying data quality, lineage, least-privilege access, and sensitive-data controls; ability to translate business and risk requirements into testable engineering controls
• Ability to 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; ability to diagnose reliability, performance, and cost issues
• Experience leading a complex technical workstream, mentoring engineers, and collaborating with client stakeholders to clarify requirements, communicate trade-offs, and support scope and effort estimates
• Ability to travel to client sites as needed

Technologies

• Python
• SQL
• Spark
• dbt
• CI/CD
• Snowflake

Preferred

• Industry experience building at a leading bank, lender, or financial services institution for operational applications
• Relevant financial services experience in consumer lending, commercial lending, or payments (mortgage, credit card, personal loan, or auto lending, including servicing, collections, and default); adjacent banking or wealth and asset management experience is a plus
• Experience building 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
• Experience evaluating vendor data capabilities or converting domain expertise into reusable data assets, accelerators, and client implementation patterns
• Experience implementing the Snowflake platform

Required (Legal/Work Authorization)

• Candidates must verify legal right to work in the United States without employer sponsorship now or in the future

Location: New York, NY (onsite)

Compensation: USD 158,000 - 210,000 per yearly