Databrick Architect (Indianapolis)

Onsite: required to live in or near Indianapolis, IN and be onsite with client on a regular basis

Data Platform Architect Role Overview We are seeking an experienced, hands-on Data Platform Architect & Integration Lead to design, build, and deliver integrations that consume Master Data products published in Databricks and feed them into an enterprise commercial CRM platform, as part of a large-scale CRM modernization program.

Master Data sources (customer, account, and product master) are exposed as governed data products in Databricks curated Delta tables registered in Unity Catalog. This role owns the consumption of those data products and their reliable, validated delivery into the target CRM and enterprise ecosystem, while re-platforming existing integrations onto modern, cloud-native patterns.

This is a working, hands-on leadership role. The person leads the integration workstream and personally performs the coding, code review, and deployment work not oversight alone. Every skill listed is expected to be backed by direct, hands-on delivery experience.

Key Responsibilities

• Hands-On Integration Delivery
• Personally perform coding, code review, and deployment activities, and any other assigned work related to this transformation leading the team does not replace individual hands-on contribution.
• Must have hands-on experience across all required skills; the person is expected to build, not only direct others who build.
• Own the end-to-end integration lifecycle: design specification, build, unit testing, deployment across dev / sandbox / UAT / prod, runbooks, and knowledge transfer.
• Produce design specifications, deployment steps, and rollback approaches, and deliver them hands-on.
• Databricks Data-Product Consumption & Integration
• Consume Databricks data products (Delta Lake tables via Unity Catalog) as the source of master data, respecting the data-product contract schema, refresh cadence, quality guarantees, and access model.
• Move master data from Databricks into the CRM using the appropriate pattern Apache Iceberg interoperability, Spark / PySpark-based movement, or API-based delivery selecting the right mechanism per integration by volume, latency, and directionality.
• Build transformation and mapping logic (PySpark / Spark SQL) to reconcile the data-product schema with the target CRM data model, including handling master-data change (deltas, data-change requests, reference data).
• Re-platform existing master-data integrations onto the Databricks-data-product and modern integration patterns, ensuring parity, reconciliation, and no data loss during transition.
• Implement observability, data-quality checks, error handling, data-gap detection, and reprocessing across the Databricks-to-CRM flows.
• Client Engagement & Meetings
• Participate in and lead all relevant client meetings design reviews, working sessions, governance forums, and status discussions representing the integration workstream directly with the client.
• Communicate technical decisions, risks, and dependencies clearly to client stakeholders and delivery teams.
• Multi-Vendor (Dual-SI) Collaboration
• Collaborate with the second System Integrator (SI) partner responsible for independently testing and validating this teams build.
• Review the second SIs test findings, triage them, and resolve the defects ensuring fast, clean cross-vendor root-cause and closure rather than back-and-forth.
• Maintain clean build-to-test handoffs and shared traceability so defects are resolved efficiently across vendors.
• On-Site Presence & Travel
• Must be present in person in Indianapolis.
• If not working from Indianapolis, must travel for all important meetings as required by the program.
• Patterns, Standards & Data Partnership
• Define reusable integration patterns and interface contracts for master-data flows that other build and assurance teams can work to.
• Partner with data-product owners and data-engineering teams to align on data-product schemas, versioning, refresh frequency, access, and change management.
• Ensure data integrity, security, privacy, and, where applicable, regulated-industry compliance across connected systems.

Must-Have Technical Skills All of the following require direct, hands-on experience:

• Databricks hands-on with Delta Lake, Unity Catalog, and consuming / publishing data products; lakehouse / medallion architecture.
• AI hands-on experience applying AI in a data, integration, or software-development context.
• Data services on AWS hands-on experience building data and transformation services / pipelines on AWS.
• API hands-on REST API design, build, and implementation at scale.
• PySpark strong, hands-on PySpark and Spark SQL for data movement and transformation.
• Master Data / MDM hands-on experience integrating customer, account, and product master data into downstream systems, including schema mapping, reconciliation, and master-data change handling.
• Demonstrated ability to lead a build team while remaining hands-on doing the build, review, and deployment work directly.