Data Architect – M4

• Reference data architectures. Canonical Azure-based blueprints, medallion
  lakehouse on Microsoft Fabric / Synapse + ADLS Gen2, operational stores on Azure
  SQL / Cosmos DB / Azure Database for MariaDB and PostgreSQL, governance and
  lineage on Microsoft Purview. Versioned, opinionated, with working sample
  implementations.
• Data modeling end-to-end. Conceptual → logical → physical. Dimensional
  (Kimball), Data Vault, medallion (bronze/silver/gold), and normalized OLTP schemas.
  You pick the right one for the job and document the why.
• ETL/ELT and data pipelines. Design and oversee pipelines using Azure Data
  Factory, Synapse pipelines, Fabric Data Engineering, and Azure Databricks with
  dbt, Spark, and Airflow where they’re a better fit. Streaming with Event Hubs, Kafka,
  or Stream Analytics where the use case demands it.
• Migration leadership. Plan and execute heterogeneous data migrations,
  including SQL Server ↔ MariaDB / MySQL / PostgreSQL — covering schema
  conversion, CDC-based low-downtime cutover (Azure Database Migration Service,
  Debezium), reconciliation, and post-migration validation. Build the migration
  playbooks the CoE reuses.
• Data Readiness for AI, the service offering. Build out and run the productized
  client offering: data quality assessments, schema rationalization, master/reference
  data, lineage and cataloging on Purview, governance scaffolding, and the playbook
  that gets a client’s data ready to power AI/ML workloads. This is something the
  practice sells; you’re the technical owner.
• AI/ML data layer in the CoE. Partner with the AI Architect on the data layer behind
  production AI: retrieval indices on Azure AI Search and Cosmos DB vector,
  document chunking and embedding pipelines, feature stores, training and eval
  dataset curation, and the data quality bar required for AI to actually work.
• Governance, quality, and lineage. Cataloging, classification, sensitive-data
  handling, and lineage on Microsoft Purview. Data quality frameworks (Great
  Expectations, Soda, or native Fabric/Synapse checks). The governance posture that
  lets us operate in regulated client environments.
• R&D and team enablement. Track what’s shipping in Azure data (Fabric, OneLake,
  Purview, AI Search) and the open-source ecosystem (Iceberg, Delta, Hudi, dbt, Spark,
  Airflow), prototype what matters, run internal workshops and labs, and level up the
  data engineering team.
• Presales partnership. Senior data voice on qualified pursuits, technical
  discovery, scoping data engagements, shaping solution architectures, contributing
  to SOWs and proposals, presenting to client data leaders and CDOs.
• Team leadership. Lead and grow a small team of data engineers. Run architecture
  reviews, code reviews, and design sessions across the CoE, and partner closely with
  client delivery teams.

What We’re Looking For

• 12+ years in data engineering and architecture roles, with hands-on production
  experience across OLTP, OLAP / analytics, and modern lakehouse workloads.
  • Deep on relational systems. Production-grade experience with SQL Server (T-SQL,
  indexing, query tuning, Always On / HA topologies, security models) and MariaDB /
  MySQL. You’ve designed schemas, tuned slow queries, and recovered systems
  under pressure.
• Migration experience. You’ve led at least one substantial heterogeneous data
  migration, cross-RDBMS, on-prem-to-cloud, or platform, including schema
  translation, data movement strategy, CDC, cutover planning, and validation.
• Deep on the Azure data stack. Microsoft Fabric (OneLake, Data Factory, Synapse
  Data Engineering, Real-Time Intelligence, Power BI), Azure Synapse Analytics, Azure
  Data Factory, Azure Databricks, Azure SQL / Managed Instance, Azure Database for
  MariaDB / MySQL / PostgreSQL, ADLS Gen2, Cosmos DB, Microsoft Purview, Event
  Hubs, Stream Analytics.
• Modeling depth. Dimensional (Kimball), Data Vault, medallion / lakehouse,
  normalized OLTP. You can defend the choice in front of a client architect.
• Strong open-source fluency. Apache Spark, Airflow, dbt, Kafka / Debezium, open
  table formats (Iceberg, Delta, Hudi), PostgreSQL. Data quality tooling like Great
  Expectations or Soda.
• AI/ML data prep experience. Building training and eval datasets, feature
  engineering, vector embedding pipelines, RAG-ready document processing, and
  partnering closely with data scientists and ML engineers.
• Production discipline. Infrastructure-as-code (Bicep / Terraform), CI/CD for data
  pipelines, observability, testing, the unglamorous things that separate prototypes
  from production data platforms.
• Presales-capable. Comfortable in front of clients: discovery, whiteboarding
  solutions, defending architecture decisions, contributing to proposals and SOWs.
• Experience leading small engineering teams. You’ve managed at least 2–3
  engineers before, balanced shipping with people development, and know when to
  step in versus step back.
• Clear technical communication. You can talk shop with a CDO and pair with a
  junior engineer in the same afternoon.

What Success Looks Like in Year One

• A published set of CoE reference data architectures, Fabric lakehouse blueprint,
  migration playbook, governance pattern with working Azure sample
  implementations.
• The Data Readiness for AI service offering is built, sold, and delivered on at least a
  handful of client engagements.
• Ownership of data migration project (including a SQL Server / MariaDB transition or
  similar heterogeneous move) executed under your architecture leadership.
• The data layer powering CoE AI systems, retrieval indices, feature data, training
  and eval sets is measurably more reliable, better-governed, and easier to extend
  than it was when you started.
• A running R&D and training cadence: regular evaluations of new Azure data and OSS
  releases, an internal workshop / lab program, and visible upskilling across the data
  engineering team.
• Direct contribution to won deals you’ve been the senior data architect on at least
  a handful of presales pursuits that closed.
• The engineering bar for data work across the CoE is visibly higher because of the
  patterns, reviews, and standards you’ve established.

Nice to Have

Microsoft certifications (Azure Data Engineer Associate, Fabric Analytics Engineer Associate, Azure Solutions Architect Expert) or equivalent depth. Open-source contributions in the data / ML space. Experience operating data platforms under regulatory constraints (HIPAA,
SOC 2, PCI, and financial services. Background in services or consulting environments where the same architecture serves multiple clients.