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.
