Senior Data Platform Architect

End-to-end platform architecture and implementation

• Design the medallion architecture: catalog and schema design layer contracts and the promotion path from development through QA to production.
• Design robust scalable data platform architectures aligned with enterprise security and networking standards.
• Act as a bridge between Data Solutions and Infrastructure ensuring the platform decisions you make are the ones BI AI data science and activation teams can actually build on.

Databricks & Unity Catalog administration

• Administer Unity Catalog: catalog bindings external locations storage credentials grants and the service-principal model behind them.
• Define workspace and compute standards cluster policies runtime versions pool lifecycle job compute versus all-purpose and serverless where it pays for itself.
• Manage platform identity end to end: users groups SCIM provisioning from Entra ID secret scopes and token governance.
• Keep the estate current: runtime upgrades deprecation tracking and retiring the pools and jobs that quietly stop working.

Ingestion orchestration & integration

• Design pipeline architecture across Azure Data Factory and Databricks Workflows: dependency design idempotency watermarking and retry and backfill semantics that survive a bad source day.
• Keep source integrations healthy Dataverse and Synapse Link exports finance and booking systems third-party feeds including the runtime connector and credential lifecycle they silently depend on.
• Establish data contracts with upstream owners so schema changes reach the platform before they reach production.
• Land a modernization decision for SQL Server to Databricks without breaking the reporting built on it.

AI-assisted engineering standards & harness enablement

• Establish and enforce the AI coding standard for the data team: what assistants may generate what must be human-reviewed before merge and what may never reach production without a test behind it.
• Build and maintain the AI coding harness repository instruction files project context MCP servers tool permissions and sandboxed environments so generated code arrives already matching our conventions.
• Establish review practice for generated code and SQL: correctness against the data model cost and performance impact and provenance recorded in the pull request.
• Continuously evaluate and improve output quality refining instructions and context based on what the team actually gets wrong treating the harness as a product with users not a config file.

AI/ML platform administration

• Administer ML assets in Unity Catalog registered models aliases feature tables and MLflow experiments including the grants and promotion path from development through QA to production.
• Define GPU and ML compute standards: node-type allowlists Azure quota management idle termination spot strategy and ML Runtime version lifecycle so expensive compute cannot be left running unnoticed.
• Govern foundation model and external LLM access through the AI Gateway rate limits usage tracking payload logging guardrails and provider credentials held in secret scopes rather than notebooks.
• Extend network and data-protection controls to AI workloads: egress rules for external model APIs retention of logged prompts and responses and handling of customer data in inference payloads.

Security access & platform governance

• Define the platforms security posture: network isolation private connectivity storage firewalls and a documented rationale for every public-access exception that remains.
• Run credential hygiene secrets into Key Vault scheduled rotation and no long-lived plaintext tokens in notebooks pipelines or configuration.
• Design and administer the access model: role definitions joiner/mover/leaver flow privileged access and periodic entitlement review.
• Work with the Infrastructure team and move platform configuration into Terraform and CI/CD and keep it there so there is no drift between what is deployed and what is declared.

Identity resolution & marketing activation

• Contribute to a consent suppression and preference management solution ensure rules are applied correctly and provably per market and that a failure in that logic is caught by the platform rather than by a customer.
• Steward the data-sharing connections into Adobe Experience Platform and activation

Reliability quality & cost

• Define SLAs for the pipelines the business actually depends on then build the monitoring and alerting that proves you are meeting them.
• Lead incident response for platform failures: triage root cause remediation and a written RCA that changes something.
  Close the class of failure that hurts most silent partial loads duplicate or missing records and jobs that report success on incomplete data.
• Support the data quality framework: rule coverage severity semantics and making sure a blocking rule actually blocks.
  Monitor platform spend: cluster right-sizing storage lifecycle and a defensible view of cost per workload.