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# Lead Data Engineer

- **Company:** Goldenpick Technologies
- **Location:** Cary, NC, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Automation of Tests, Microsoft Azure, Computer Programming, Continuous Integration, Information Engineering, Data Profiling, Graph Database, Information Retrieval, Python (Programming Language), Metadata, Neo4j, Performance Tuning, Posix, Prism (Software), Role-Based Access Control, OpenAI, Standard Sql, Azure Data Lake, Scala (Programming Language), SPARQL, SQL Databases, Data Streaming, Management of Software Versions, Datadog, Azure Data Factory, Great Expectations (Foster Youth College-readiness and Support Program), Retrieval-Augmented Generation, Prompt Engineering, Apache Spark, Llamaindex, Generative AI, Data Lakes, Pyspark, Machine Learning Operations, Terraform, Software Version Control, Data Pipelines, Workday, Key Vault, Databricks
- **Published:** October 4, 2026
- **Apply:** https://www.dice.com/job-detail/9b7db18a-9634-421d-ac4b-905c3b03b158

## About the Role

Programming & Data Engineering

 * Expert - level proficiency in Python, Scala, and PySpark, with a strong track record of designing and delivering production-ready, modular, and well-tested solutions; developing and troubleshooting Spark workloads; and optimizing large-scale batch and streaming data pipelines using Delta Lake and Spark technologies.
 * Strong SQL and data modelling - dimensional and normalised; schema design and data contract definition.
 * Databricks expertise - Delta Lake, Unity Catalog, Jobs & Workflows, cluster and pool management, performance tuning, Model Serving.
 * Azure data stack - ADLS Gen2 (zone design, ACLs, lifecycle), Azure Data Factory (parameterized / metadata-driven frameworks, error handling), Azure Event Hubs.

AI & Machine Learning

 * 3+ years designing and shipping LLM-based systems in production: RAG pipelines, agentic / tool-calling workflows, structured output, chunking and embedding strategy, vector and hybrid retrieval, and prompt engineering.
 * Evaluation discipline - golden datasets, regression suites, accuracy and hallucination tracking, human-in-the-loop feedback loops; you measure AI quality, not assert it.
 * Hands-on experience with LangChain, LlamaIndex, or LangGraph, plus at least one provider stack (Azure OpenAI, OpenAI, or Databricks Model Serving).
 * Metadata-driven thinking - schema inference, data profiling, lineage, catalogs, and configuration-driven frameworks that onboard the next source without new code.

Architecture & Governance

 * 12-18 years of total experience in data engineering, data platform delivery, or related disciplines.
 * Proven delivery of a medallion / lakehouse architecture at enterprise scale - not just familiarity with the concept.
 * Azure security and governance - Entra ID, managed identities, RBAC, POSIX ACLs on ADLS Gen2, Key Vault, private endpoints, and PII handling.
 * CI/CD and infrastructure as code - Azure DevOps, Terraform, Databricks Asset Bundles, and automated testing of data pipelines.
 * Clear technical writing and the ability to present and defend a design to both engineers and non-technical stakeholders.

## Description

* Knowledge graphs and ontologies: RDF/SPARQL, property graphs (Neo4j), or graph modelling over a lakehouse.
 * Text-to-SQL or semantic-layer-backed natural-language query systems at enterprise scale, including access control and ambiguity handling.
 * ML-based anomaly detection on time-series or transactional financial data.
 * Financial services or insurance domain knowledge: finance close, general ledger, subledger, reconciliation, or actuarial data.
 * LLMOps and MLOps: model versioning, prompt versioning, cost governance, and observability tooling.
 * Databricks Data Engineer Professional, Azure DP-203 / DP-700, or AZ-305 certification.
 * dbt, Great Expectations, or similar data-quality and transformation tooling.
 * Workday, Prism, or Accounting Center exposure.