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Lead Data Engineer/Data Architect
Job in
Cary, Wake County, North Carolina, 27518, USA
Listed on 2026-10-15
Listing for:
Enexus Global Inc.
Full Time
position Listed on 2026-10-15
Job specializations:
-
Software Development
Data Engineering
Job Description & How to Apply Below
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.
- 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.
- 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 Lang Chain, Llama Index, or Lang Graph, 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.
- 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 , managed identities, RBAC, POSIX ACLs on ADLS Gen2, Key Vault, private endpoints, and PII handling. - CI/CD and infrastructure as code
- Azure Dev Ops, 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.
- 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.
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