Data Engineer / Data Modeler

About The Role We're looking for a Senior Data Engineer / Data Modeler who can both build scalable data pipelines and design the data models that make that data usable for BI, analytics, and GenAI use cases. You'll work directly with Fortune 500 clients across industries in small, high-ownership pods that blend consulting rigor with hands-on engineering. This is a fully remote role.

Key Responsibilities

• Design and build scalable batch/streaming pipelines to ingest, transform, and deliver data into cloud platforms.
• Design conceptual, logical, and physical data models (dimensional, relational, semantic) for BI, analytics, and AI consumption.
• Translate business requirements into star/snowflake schemas and ER diagrams with architects and governance teams.
• Build and maintain data warehouses/lakehouses on Azure, AWS, GCP, or Snowflake.
• Implement data quality, validation, and reconciliation frameworks.
• Enforce data modeling standards and documentation practices.
• Tune pipeline and query performance for large-scale datasets.
• Structure data for ML/GenAI use cases in collaboration with data science teams.
• Work across the full SDLC with strong engineering practices (version control, CI/CD, code review).
• Present and defend data model designs to technical and business stakeholders, including mentoring junior engineers.

Required Qualifications

• Bachelor's in Computer Science, Engineering, or related field (Master's preferred).
• 7-10 years in data engineering, including 4+ years of hands-on data modeling (relational and dimensional).
• Healthcare domain experience is required.
• Experience in a consulting or client-facing delivery environment is a plus.

Required Technical Skills

• Languages: Advanced SQL (T-SQL/PL-SQL), Python
• Data Modeling: Dimensional modeling, star/snowflake schemas, ER diagramming, data warehouse design
• Cloud: Azure (ADF, Synapse, Databricks) and/or AWS (Glue, Redshift) and/or GCP (BigQuery)
• Big Data: Spark, Kafka
• Platforms: Snowflake, Databricks
• ETL/ELT: ADF, SSIS, dbt
• Engineering: Git, Terraform, Docker, CI/CD

Preferred Skills

• Semantic layer design for self-service and AI-ready analytics
• Exposure to feature stores or vector databases for RAG/LLM pipelines
• Data governance/catalog tools (Purview, Collibra, Atlas)
• BI tools (Power BI, Tableau) from a modeling standpoint

Good-to-Have Skills

• Exposure to Azure OpenAI, AWS Bedrock, or Vertex AI
• MLOps familiarity
• Bash/PowerShell scripting
• API-based/event-driven data integration

Soft Skills

• Strong stakeholder communication and ability to simplify complex models
• Ownership mindset and comfort with ambiguity
• First-principles problem-solving
• Documentation discipline and attention to detail

Preferred Certifications

• Microsoft Azure Data Engineer Associate (DP-203)
• SnowPro Core/Advanced: Data Engineer
• Databricks Certified Data Engineer
• Google Professional Data Engineer