Lead Data Engineer

Lead Data Engineer

Cary, NC (On-site)

Experience: 12-18 years

Employment: Full-Time

Eligibility: US Citizens only

ABOUT THE ENGAGEMENT

A centralized, AI-first enterprise Data Hub for a global insurance and financial services client on Azure Databricks. The platform ingests 150 inbound data feeds, distributes to 35 downstream systems, and is organized as a medallion architecture (Bronze / Silver / Gold). AI is embedded in ingestion, canonical mapping, data quality, reconciliation, and business user access from day one.

This is a senior hands-on leadership role. The candidate will own the end-to-end technical design of the data and AI layers, build reference implementations for the engineering team, and ship production-grade Python, Scala, and PySpark code every week. Candidates who have not written or reviewed production code in the past year are not a fit.

WHAT THE ROLE OWNS

Data platform architecture and engineering: lakehouse architecture (Bronze / Silver / Gold contracts, ADLS Gen2 zone layout, Delta Lake table design, partitioning, schema evolution, retention).

Metadata-driven, parameterized ingestion frameworks for batch files, database extracts, CDC feeds and streaming (Azure Event Hubs / Kafka, Spark Structured Streaming).

Canonical PySpark and Scala Spark jobs, coding and testing standards, PR reviews, production incident debugging, Spark cluster tuning and cost guardrails.

CI/CD for Databricks and ADF in Azure DevOps using Databricks Asset Bundles and Terraform; observability with Azure Monitor and Log Analytics.

AI-augmented ingestion and canonical mapping: auto-generated bridge documents, DML, canonical table definitions; AI-assisted source-to-canonical mapping with human review gate.

AI-driven data quality, anomaly detection (data drift, schema drift, volume shifts, reconciliation breaks), automated reconciliation, and synthetic privacy-preserving test data.

Semantic layer and knowledge graph, plus a GPT-powered conversational interface (text-to-SQL / semantic-layer retrieval) with row- and column-level security.

Governance and leadership: Unity Catalog (lineage, access control, PII standards), Architecture Review Boards and AI governance forums, mentoring engineers, documentation.

MUST-HAVE SKILLS & EXPERIENCE

Expert-level Python, Scala and PySpark: production-ready, modular, well-tested solutions; Spark workload troubleshooting; optimizing large-scale batch and streaming pipelines using Delta Lake.

Strong SQL and data modelling (dimensional and normalised), schema design, data contracts.

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), Azure Event Hubs.

3 years designing and shipping LLM-based systems in production: RAG pipelines, agentic / tool-calling workflows, chunking and embedding strategy, vector and hybrid retrieval, prompt engineering.

Evaluation discipline: golden datasets, regression suites, accuracy and hallucination tracking, human-in-the-loop feedback.

Hands-on with LangChain, LlamaIndex or LangGraph, plus at least one provider stack (Azure OpenAI, OpenAI, or Databricks Model Serving).

Metadata-driven frameworks: schema inference, data profiling, lineage, catalogs.

12-18 years of total experience in data engineering / data platform delivery.

Proven enterprise-scale delivery of a medallion / lakehouse architecture.

Azure security and governance: Entra ID, managed identities, RBAC, POSIX ACLs, Key Vault, private endpoints, PII handling.

CI/CD and IaC: Azure DevOps, Terraform, Databricks Asset Bundles, automated testing of data pipelines.

Clear technical writing and ability to present and defend designs to engineers and non-technical stakeholders.

STRONGLY PREFERRED

Knowledge graphs and ontologies (RDF/SPARQL, Neo4j, graph modelling over a lakehouse).

Text-to-SQL or semantic-layer-backed natural-language query systems at enterprise scale.

ML-based anomaly detection on time-series or transactional financial data.

Financial services or insurance domain (finance close, GL, subledger, reconciliation, actuarial data).

LLMOps / MLOps: model and prompt versioning, cost governance, observability.

Databricks Data Engineer Professional, Azure DP-203 / DP-700, or AZ-305 certification.

dbt, Great Expectations or similar; Workday, Prism or Accounting Center exposure.