Databricks 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 team’s build.

Review the second SI’s 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.

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.