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Iris Software Inc. Linkedin · Posted 5d ago

Data Engineer

Roseland, New Jersey, United States

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Indexed description


Iris's direct client, a leader Payroll, is looking to hire a strong Databricks Platform Engineer for a Long Term Contractual role.


Job title: Data & AI Platform Engineer — (Databricks + AWS)

Location: Hybrid (3 days onsite, Roseland, NJ)

Duration: 18 Months

Skills: Databricks, Pyspark, Python, Genie Space Or Genie Agent



Job Description


About the Role

Own the Databricks lakehouse layer and AI/LLM platform for Finance Insights. You'll design the data architecture on Databricks Unity Catalog, build Genie Spaces for natural language analytics, and integrate LLMs to power intelligent financial workflows — all running on AWS.



Responsibilities

  • Design and build Delta Lake tables on Databricks (bronze/silver/gold medallion architecture) for financial datasets (AR, GL, invoices, collections)
  • Build and manage Databricks Genie Spaces — configure semantic models, write GENIE-compatible SQL, and tune NL-to-SQL behavior for finance use cases
  • Develop PySpark and Python-based data pipelines (Databricks Jobs + Workflows) for ERP data ingestion, transformation, and aggregation
  • Integrate Databricks Model Serving with LLMs (Claude, GPT-4, or custom fine-tuned models) for AI-driven insights, anomaly detection, and financial forecasting
  • Build RAG pipelines using Databricks Vector Search over financial documents, contracts, and GL notes
  • Expose Databricks SQL Warehouse endpoints to Python API services and the React frontend
  • Manage Unity Catalog governance — row-level security, column masking, data lineage for finance compliance
  • Deploy infrastructure on AWS (S3 as Databricks external storage, IAM roles, VPC networking, Secrets Manager)


Required Skills

  • Databricks (PySpark, Delta Lake, Jobs/Workflows, SQL Warehouses) — 3+ years
  • Databricks Unity Catalog and governance
  • Databricks Genie Spaces (semantic layer, NL analytics)
  • Python 3.11+ for data engineering and API integration
  • AWS (S3, IAM, VPC, Secrets Manager, optionally Lambda/Glue)
  • LLM integration: Databricks Model Serving, MLflow, or direct API calls to Claude/OpenAI
  • SQL (advanced — window functions, CTEs, query optimization)


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