Data Modeling/ Data Architect - Contract W2 Position

Job Title: Data Modeling/ Principal Data

Location: Remote

Duration: 6 Month s Contract

Job Description:
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

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 model.