Job Summary
We are looking for a Senior Data Platform Architect with strong end-to-end data and platform skills to design, operate, and secure the Azure Databricks that powers finance, revenue management, marketing, and booking and reservation across the business. This architect will report to Director, Customer Data and Identity Engineering.
Beyond design, this role will cover the platform administration in North America including Unity Catalog governance, access and credentials, pipeline reliability, and cost — and turns it into something documented, automated. You will also set the standard for how AI-assisted coding development is used across the data team, and define the tooling that makes it safe.
Job Responsibilities:
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 platform's 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.
- Strong end-to-end data platform experience across architecture, administration, and production operations.
- Databricks Platform Design and Administration — Expert: hands-on with Unity Catalog (metastores, external locations, storage credentials, grants), cluster policy, workspace configuration, and identity federation — not notebook development alone.
- ML platform administration — Strong working knowledge: Models in Unity Catalog, MLflow, Mosaic AI Model Serving, and GPU cluster policy and quota management on Azure.
- SQL — Expert: able to design, optimize, and troubleshoot complex analytical queries; deep understanding of joins, CTEs, window functions, aggregations, and performance tuning.
- Python — Advanced: production-grade data transformation and automation with PySpark; strong understanding of data quality, testing, and scalable processing in Databricks.
- Azure platform — Strong working knowledge: ADLS Gen2, Data Factory, Entra ID, Key Vault, virtual networks and private connectivity, and RBAC design.
- Infrastructure as code — Working knowledge: Terraform in practice, with Git-based CI/CD and an intolerance for undeclared manual changes.
- Demonstrated ownership of a production platform: you have been on the hook when it broke, and you fixed the class of problem rather than the instance.
- Practical data governance and access control experience, including privacy or consent handling across more than one market.
- Experience working with financial / accounting data and the reconciliation scrutiny that comes with it.
- Proven ability to communicate design decisions and trade-offs clearly to both engineers and business stakeholders, and to say no to a bad design with a reason attached.
Nice to have / strong plus
- Hands-on Adobe Experience Platform, or a comparable CDP / identity-resolution stack (LiveRamp, Snowflake, Salesforce CDP).
- Snowflake, Synapse, or Microsoft Fabric operated alongside Databricks.
- MLOps delivery with Databricks Asset Bundles or equivalent, and drift and quality monitoring over inference tables.
- D365 or another ERP and finance integration under real accounting scrutiny.
- Experience retiring a legacy SQL Server estate without breaking the reports built on top of it.
- dbt, or another framework for layered models, testing, and documentation.
- Experience with booking curves, demand patterns, or revenue analytics in hospitality, cruise, travel, or another high-volume consumer booking domain.
- Databricks or Azure certification (Data Engineer Professional, Azure Solutions Architect Expert).
Prior experience as a platform owner or technical lead
Salary range $155,000-$180,000 annually determined by a myriad of factors including, but not limited to, years of experience, depth of experience, and other relevant business considerations.
Full benefits including medical, dental, vision, life and disability insurance at a highly subsidized rate (some plans are fully paid by Viking).
Discounts on theatres, theme parks, movie tickets, travel discounts through IATA membership and too many more discounts to name.
Viking is a certified Great Place to Work company. This certification is a result of our commitment to excellence, integrity and our teams’ outstanding contributions.
Viking (NYSE: VIK) is a global leader in experiential travel with a fleet of more than 100 ships, exploring 21 rivers, five oceans and all seven continents. Designed for curious travelers with interests in science, history, culture and cuisine, Chairman and CEO Torstein Hagen often says Viking offers experiences For The Thinking Person™. Viking has more than 450 awards to its name, including being rated #1 for Rivers and #1 for Oceans five years in a row by Condé Nast Traveler in the 2025 Readers’ Choice Awards. Viking is also rated a “World’s Best” by Travel + Leisure—no other travel company has simultaneously received such honors by both publications.