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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Data Engineer - Azure Databricks - **Company:** MANASOTA-SOLVE, INC. - **Location:** Cary, NC, United States - **Experience:** Expert - **Salary:** $140,000.0 - $145,000.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, AI Evaluation, Artificial Intelligence, Automation of Tests, Microsoft Azure, Databases, Continuous Integration, Information Engineering, Data Governance, Data Retention, Graph Database, Information Retrieval, Python (Programming Language), Metadata, Neo4j, Performance Tuning, Role-Based Access Control, Resource Description Framework (RDF), Regression Testing, OpenAI, Standard Sql, Azure Data Lake, Scala (Programming Language), Search Technologies, SPARQL, SQL Databases, Data Streaming, Management of Software Versions, Data Ingestion, Azure Data Factory, Great Expectations (Foster Youth College-readiness and Support Program), Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Apache Spark, Llamaindex, Agentic-AI, Data Lakes, AI Platforms, Pyspark, Production Code, Apache Kafka, Spark Streaming, Data Management, Machine Learning Operations, Software Coding, Terraform, Data Pipelines, Human in the Loop, Key Vault, Databricks - **Published:** October 7, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=aacfdca8fae72ca6 ## About the Role * 12-18 years of professional experience in Data Engineering, Data Platforms, or related fields. * Expert-level proficiency in Python, Scala, and PySpark. * Strong SQL and data modeling experience, including dimensional and normalized data models. * Extensive hands-on experience with Azure Databricks and Delta Lake. * Strong knowledge of Databricks Jobs & Workflows, Unity Catalog, cluster management, and performance tuning. * Experience with Azure Data Lake Storage Gen2 (ADLS Gen2). * Strong experience with Azure Data Factory and metadata-driven pipeline frameworks. * Experience with Azure Event Hubs, Kafka, or Spark Structured Streaming. * Proven experience designing and delivering enterprise-scale Lakehouse / Medallion architectures. * At least 3 years of hands-on experience designing and deploying production LLM/AI systems. * Strong understanding of RAG, embeddings, vector/hybrid retrieval, prompt engineering, and agentic workflows. * Experience with LangChain, LlamaIndex, or LangGraph. * Experience with at least one AI platform such as Azure OpenAI, OpenAI, or Databricks Model Serving. * Experience implementing AI evaluation frameworks, golden datasets, regression testing, and human-in-the-loop validation. * Strong understanding of Azure security, including Entra ID, RBAC, Managed Identities, Key Vault, ACLs, and Private Endpoints. * Experience with Azure DevOps, Terraform, CI/CD, and automated testing. * Excellent communication, documentation, problem-solving, and stakeholder management skills. Preferred Qualifications * Experience with Knowledge Graphs and Ontologies. * Knowledge of RDF, SPARQL, Neo4j, or graph-based data modeling. * Experience developing enterprise-scale Text-to-SQL or semantic-layer solutions. * Experience with ML-based anomaly detection for transactional or time-series data. * Previous experience in Financial Services or Insurance. * Experience with LLMOps / MLOps, model and prompt versioning, cost management, and observability. * Experience with dbt, Great Expectations, or similar data quality frameworks. * Databricks Data Engineer Professional certification. * Azure DP-203, DP-700, or AZ-305 certification. What We Are Looking For We are looking for a senior engineer who combines strong architecture skills with hands-on coding ability. Candidates should be actively involved in production engineering and comfortable taking ownership from technical design through implementation, testing, deployment, and production support. Candidates who have not written or reviewed production code recently may not be suitable for this position. ## Description We are seeking a highly experienced and hands-on Lead Data Engineer to join an enterprise-scale data and AI platform initiative. The successful candidate will be responsible for the architecture, development, optimization, and delivery of modern data engineering solutions using Azure Databricks, PySpark, Scala, Python, Delta Lake, and Azure data services. This is a hands-on technical leadership role. The ideal candidate must have recent experience writing, reviewing, and troubleshooting production code and should be comfortable working across both data engineering and AI/LLM solutions., * Design and implement scalable Lakehouse / Medallion Architecture using Bronze, Silver, and Gold layers. * Develop production-grade data pipelines using Python, Scala, PySpark, Spark Structured Streaming, and Delta Lake. * Build metadata-driven and parameterized ingestion frameworks for batch, database extracts, CDC, and streaming data. * Work with Azure Databricks, ADLS Gen2, Azure Data Factory, Azure Event Hubs, and Kafka. * Design Delta Lake tables, data contracts, partitioning strategies, schema evolution, and data retention processes. * Troubleshoot and optimize Spark workloads, clusters, pipelines, and overall platform performance. * Establish coding standards, automated testing practices, and conduct code/PR reviews. * Implement CI/CD pipelines using Azure DevOps, Terraform, and Databricks Asset Bundles. * Develop AI-assisted solutions for data ingestion, canonical mapping, data quality, and reconciliation. * Design and implement production-grade LLM, RAG, and agentic AI workflows. * Work with embeddings, vector search, hybrid retrieval, prompt engineering, and tool-calling architectures. * Develop semantic-layer and text-to-SQL capabilities for enterprise data access. * Implement data governance, lineage, access control, and PII protection using Unity Catalog. * Monitor data quality, schema drift, data anomalies, reconciliation issues, and pipeline health. * Mentor data engineers and provide technical guidance on architecture, development, and best practices. * Present and defend technical designs to engineering teams, architects, and business stakeholders.