AI Software Lead, Data & MDM

You will use agentic AI - coding agents, retrieval and reconciliation agents, and evaluation tooling - as a primary way to move faster on data ingestion, matching, cleanup, and validation. Your value is not the speed of the tooling. It is the judgment to define what “correct” means for a given piece of master data, constrain agents to work within that definition, and prove the result is right before it touches a system of record. A meaningful part of the role is designing the data contracts and validation rules that keep agent-driven reconciliation honest: what a valid record looks like, what counts as a confident match versus one that needs a human, and what gets logged so a decision can be audited later. A typical week may include:

• Designing or refining the data model and validation rules for a core master data domain (for example, customer, contract, or location), in partnership with the business owner of that domain.
• Building or extending agent-assisted pipelines that ingest, match, and reconcile records across systems such as CRM and ERP platforms, with human review gates for low-confidence matches.
• Measuring data quality - duplication, completeness, and conflict rates - and building tooling that makes those metrics visible to data owners.
• Building evals, guardrails, and audit logging for AI-assisted data workflows so decisions can be explained and traced after the fact.
• Working with data owners, architecture, security, and governance stakeholders to translate policy decisions (what a record must contain, who can create one) into enforced systems.

• Strong understanding of Product Management, Business Analysis, Analytics, and enterprise data lifecycles.
• 5+ years building enterprise data solutions, including hands-on experience with master data management: entity resolution, record matching or deduplication, survivorship rules, or golden-record design.
• Experience integrating and reconciling master data across CRM and ERP systems (for example Salesforce, JD Edwards, SAP, or similar), including situations where systems disagree or have inconsistent access controls.
• Experience with data ingestion, transformation, integration, and governance frameworks, including defining data-quality rules in partnership with business data owners, not only implementing rules handed to you.
• Experience developing with Python and building production-grade AI and data workflows.
• Experience with agent frameworks, RAG, vector databases, embeddings, context engineering, and tool-calling patterns.
• Experience with LLM observability, evaluation frameworks, prompt optimization, and guardrails, including building audit trails and human sign-off gates for agent-driven decisions on business-critical data.
• Experience with AWS services relevant to AI and data workloads, including Bedrock, Lambda, ECS/Fargate, API Gateway, S3, DynamoDB, and RDS.

Nice to Have Skills & Experience

• Experience diagnosing and improving a stalled or troubled enterprise data program, not only building new ones.
• Experience with a modern cloud data platform such as Snowflake or equivalent (certifications a plus).
• Experience with Power BI and modern analytics platforms.
• Experience standing up or working within a data governance operating model: data ownership RACI, stewardship councils, or data-quality SLAs.
• Experience with geospatial, routing, logistics, transportation, or operations data.
• Experience applying AI within regulated enterprise environments, including FERPA-relevant or similarly regulated data.
• Relevant AWS, AI, cloud, or data certifications.

Benefit packages for this role will start on the 1st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.