Principal Data Architect (REMOTE)

WHAT WE'RE LOOKING FOR

Must-Have Experience

• 15+ years of hands-on data engineering and data architecture experience, with a track record of owning enterprise-scale platforms end to end.
• Deep data modeling expertise — dimensional, normalized, and Data Vault — with real judgment about grain, conformance, and when each pattern applies.
• Proven experience architecting lakehouse and/or data mesh platforms: Databricks, Delta Lake, PySpark, Snowflake, Kafka, Spark Structured Streaming, and cloud-native data services (AWS, Azure).
• Strong command of SQL and performance engineering: query optimization, partitioning and clustering, and cost management at scale.
• Demonstrated ownership of data governance, data quality, master data, metadata, and lineage programs — not just the tooling, but the operating model.
• Experience with data security and compliance in regulated industries (financial services, payments, cybersecurity, healthcare).
• Experience migrating legacy on-prem warehouses and ETL estates to cloud-native platforms.
• Working familiarity with the data requirements of ML and AI systems — feature pipelines, training data curation, and retrieval/vector data layers — enough to architect for them credibly and partner well with AI teams.

Technical Skills

• Expert: SQL, Python, PySpark, data modeling, Databricks, Delta Lake, Snowflake, Kafka, Spark Structured Streaming, AWS (S3, Glue, Redshift, Kinesis, EKS, Lambda), Airflow, Terraform, Git-based CI/CD.
• Strong: Azure data services, dbt, data quality frameworks (Great Expectations or equivalent), catalog and lineage platforms (Unity Catalog, Collibra, Alation, Purview), MDM tooling, BI semantic layers (Power BI, Tableau, Looker), Docker, Kubernetes, FastAPI, observability tooling (Grafana, CloudWatch).
• Familiar: MLflow, feature stores, vector databases (OpenSearch, Pinecone, FAISS, ChromaDB), knowledge graphs (Neo4j), LLM APIs and RAG frameworks (LangChain, LlamaIndex, AWS Bedrock, Claude, OpenAI).

The Right Mindset

• You think in domains and contracts: every design decision considers all current and future consumers, not just the one request in front of you.
• You are as comfortable in a governance and stewardship session as you are debugging a skewed Spark join late at night.
• You believe the hard part of data work is agreement, not technology — and you do the work of getting teams to a single definition.
• You close the loop: nothing is shipped until it has tests, monitoring, documentation, and a named owner.
• You are pragmatic about AI — enthusiastic about enabling it, unwilling to let it bypass governance.

PREFERRED QUALIFICATIONS

• BS / MS in Computer Science, Information Systems, Data Engineering, or a related quantitative field.
• Prior experience at a global financial institution (payments, risk, AML, compliance) or large enterprise SaaS, operating under strict regulatory oversight.
• Experience in presales solutioning for large data programs: RFP/RFI responses, SOW shaping, effort estimation, and CXO-level solutioning.
• Certifications such as Databricks Data Engineer Professional, AWS Certified Data Engineer or Solutions Architect, or Snowflake SnowPro Advanced Architect.
• Familiarity with emerging standards for AI and agent data access (e.g., MCP — Model Context Protocol) and agent entitlement models.

TECH STACK YOU'LL WORK WITH

• Core data platform: Snowflake · Databricks · Delta Lake · PySpark · Spark Structured Streaming · Kafka · Apache NiFi · Airflow · dbt · SQL · Python · AWS (S3, Glue, Redshift, Kinesis, EKS, Lambda) · Azure · Terraform · Docker · Kubernetes · GitHub Actions · Jenkins · Grafana / CloudWatch
• Governance & quality: Unity Catalog · Collibra / Alation / Purview · Great Expectations · MDM platforms · column-level lineage tooling · Power BI / Tableau / Looker semantic layers
• AI enablement: MLflow · feature stores · OpenSearch / Pinecone / FAISS / ChromaDB · Neo4j · LangChain / LlamaIndex · AWS Bedrock · Claude · MCP