Lead Data Engineer – Azure Databricks

Solvimate is advancing an enterprise-scale data and AI platform initiative and is hiring a Lead Data Engineer to drive end-to-end engineering across the modern data stack. This hybrid role in Cary, NC focuses on architecture, development, optimization, and delivery of production-grade data solutions, with an emphasis on Azure Databricks and related Azure data and AI tooling.

In this position, you will design scalable data foundations and build the ingestion, governance, and AI workflow capabilities that enable reliable access to enterprise data. You will also mentor engineers and present technical designs to both engineering teams and business stakeholders.

Responsibilities

• 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, including 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 and 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 with technical guidance on architecture, development, and best practices.
• Present and defend technical designs to engineering teams, architects, and business stakeholders.

Requirements

• 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.

Technologies

• Azure Databricks
• PySpark
• Scala
• Python
• Delta Lake
• Azure data services
• Lakehouse / Medallion Architecture
• Spark Structured Streaming
• Azure Data Lake Storage Gen2 (ADLS Gen2)
• Azure Data Factory
• Azure Event Hubs
• Kafka
• Azure DevOps
• Terraform
• Databricks Asset Bundles
• LLM
• RAG
• Unity Catalog
• embeddings
• vector search
• hybrid retrieval
• prompt engineering
• tool-calling architectures
• text-to-SQL
• LangChain
• LlamaIndex
• LangGraph
• Azure OpenAI
• OpenAI
• Databricks Model Serving
• Entra ID
• RBAC
• Managed Identities
• Key Vault
• ACLs
• Private Endpoints
• Databricks Jobs & Workflows
• CI/CD
• automated testing

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.

Salary and Location

• Location: Cary, NC (hybrid)
• Salary: USD 140,000 - 145,000 per yearly

Application Requirements

• Full Name
• Current Location
• Contact Number
• Email Address
• Work Authorization – US Citizen
• LinkedIn Profile
• Availability to Start
• Interview Availability for the Next 3 Days
• Updated Resume

What we are looking for: A senior engineer with strong architecture skills and hands-on coding ability, comfortable owning delivery 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.