JOB DESCRIPTIONLead Data ArchitectEngagement6-week fixed-scope consulting engagement (Phase 1 Data Platform)Time commitment~60 hours total; ~10 hrs/wk average, peaking at ~16 hrs/wk in Stage 2LocationRemote-first, with on-site sessions in Chambersburg, PA at stage boundaries as neededStartWithin ~72 hours of selection; name confirmed before kickoffAbout the RoleA Pennsylvania community bank (~300 employees, ~80,000 accounts) needs an enterprise data strategy, a modern target architecture, and a data warehouse platform decision before it builds AI use cases such as a Relationship Manager Copilot and automated underwriting. The Lead Data Architect is the senior technical voice on the engagement. You personally author the data strategy, governance model, and architecture, with no handoff between a strategy team and an architecture team, and you present them to the CIO and executive leadership.
This is an assess-and-design engagement. It changes no production systems.Responsibilities
Validate the source landscape (core banking, wealth and trust, Salesforce Financial Services Cloud and Marketing Cloud, three loan origination systems, MuleSoft) and score it against a seven-dimension data maturity model: strategy, architecture, integration, data quality, governance, talent and operating model, and AI enablement.
Design a five-layer target data architecture at level 1 to 2 detail, including how Salesforce fits as an integration entry point rather than the system of record.
Run a costed, vendor-neutral platform evaluation of SQL Server, Snowflake, and Databricks against ~80,000 accounts, the bank's existing SQL Server and Windows skills, a co-located private-cloud environment, MuleSoft and Salesforce integration effort, vector and RAG workload support, and licensing cost.
Define the target grain for Customer 360 (household, customer, or account) and set the entity-resolution direction for Phase 2.
Recommend the public-versus-private LLM posture and the Salesforce and MuleSoft coupling decision, each with rationale and a cost model.
Set the data-quality method for ~30 Critical Data Elements and review the analyst's profiling results, including ETL and vector-ETL effort sizing.
Extend the bank's existing AI governance committee and policy to cover data ownership, stewardship, quality standards, human-in-the-loop review, and audit logging. Name where fair-lending and adverse-action controls plug in for later underwriting work.
Author the data strategy and one-page executive summary, and convert the maturity gaps into a sequenced, costed Phase 2 build roadmap with a talent plan sized to a four-person data and analytics team.
Lead technical showcases at the end of each stage and the Phase 2 gate walkthrough with leadership.
Coach the bank's Salesforce-strong analysts during working sessions so the team inherits capability, not only documents.
Required Qualifications
10+ years in data architecture, data strategy, or enterprise data platform roles, including recent hands-on work designing a cloud or hybrid data warehouse or lakehouse.
Direct experience with at least two of SQL Server, Snowflake, and Databricks, and the ability to compare all three objectively on cost, skills fit, and scale.
Experience in banking or financial services: core banking, lending, deposits, wealth or trust data, and regulatory expectations around data (examiner review, model risk, fair lending).
Experience authoring data governance frameworks (ownership, stewardship, quality standards, controls), ideally aligned to DAMA-DMBOK or similar.
Working knowledge of integration patterns and tools (MuleSoft or comparable iPaaS, ETL/ELT) and of Salesforce as a data source.
Proven ability to brief executives and a non-technical CEO/COO audience as well as a technical project owner.
Strong written deliverable skills: strategy documents, maturity scorecards, architecture diagrams, and costed roadmaps.
Preferred Qualifications
Community or regional bank experience, and a track record of right-sizing architecture for a small data team.
Familiarity with FIS core banking and trust and wealth platforms such as InfoBanc.
Experience with vector ETL, RAG, and LLM data prerequisites, and with fair-lending proxy risk in AI and underwriting data.
Prior engagements that ended in a funded Phase 2 build.
Relevant certifications (for example CDMP, Snowflake, Databricks, or a cloud architecture certification).
Engagement Requirements
Passes vendor onboarding before kickoff: security and vendor-risk review, background check, NDA, and insurance certificates.
Works on a client-imaged laptop with VPN and MFA, using named read-only credentials. No data leaves the client environment except as agreed.
U.S.-based and onshore-only access may be required by the client's policy.