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

Location: New York, NY; preferred locations include Chicago, Washington, D.C., Dallas, and Boston. Strong candidates in other locations may be considered case by case. Ability to travel to client sites as needed. Candidates must be able to verify their legal right to work in the United States without employer sponsorship now or in the future.

Managing Consultant salary range: $158,000–$180,000; Senior Managing Consultant salary range: $190,000–$210,000.

Company

hirify.global is launching a Financial Services Operations & Technology practice combining financial services expertise with AI-enabled delivery to improve operations across lending, payments, and asset and wealth management.

What you will do

• Lead data engineering workstreams from discovery through production handover, managing 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 source traceability.
• Design warehouse and lakehouse models for operational reporting, analytics, and AI use cases, while implementing reconciliation controls, data quality checks, lineage, access controls, and PII handling.
• Prepare retrieval-ready data through document parsing, chunking, metadata enrichment, embedding pipelines, and access-aware indexing in collaboration with AI engineers.
• Build automated tests, deployment pipelines, monitoring, and recovery procedures; mentor engineers, review designs and code, collaborate across AI, application, change, and risk disciplines, and contribute reusable schemas, pipeline components, implementation guides, estimates, and proposal data dependencies.

Requirements

• At least 5 years of applicable experience for the Managing Consultant level or at least 7 years for the Senior Managing Consultant level.
• Senior, hands-on experience delivering production data pipelines and data products, with equivalent practical experience accepted regardless of formal education or certification.
• Strong Python and SQL skills, including designing, debugging, optimizing, and testing 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 Spark, dbt, or comparable tools.
• Production experience with data quality, lineage, least-privilege access, sensitive-data controls, 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, source provenance, and access permissions.
• Experience delivering maintainable data services using version control, automated testing, CI/CD, monitoring, and cloud infrastructure, including diagnosing reliability, performance, and cost issues.
• Ability to lead complex technical workstreams, mentor engineers, collaborate directly with client stakeholders, communicate trade-offs, and provide credible scope and effort estimates.
• Ability to travel to client sites as needed and legal authorization to work in the United States without current or future employer sponsorship.

Nice to have

• Experience building operational applications at a leading bank, lender, or financial services institution.
• Experience in consumer lending, commercial lending, payments, mortgage, credit card, personal loan, auto lending, servicing, collections, default, banking, wealth management, or asset management.
• 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 Snowflake.

Culture & Benefits

• Opportunity to help build a new practice within an established consulting firm.
• Collaboration with client teams, domain experts, AI specialists, change and risk practitioners, vendor partners, and the AI Co-lab.
• Work focused on trusted, reusable data assets, governed AI solutions, measurable client operating outcomes, and maintainable implementations.
• Culture centered on agility, connectivity, interdisciplinary collaboration, and diverse technical and professional perspectives.