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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Databricks Data Engineer / Databricks Architect - **Company:** Databricks - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Data Analysis, Microsoft Azure, Big Data, BigQuery, Cloud Computing, Cluster Analysis, Code Review, Computer Programming, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Data Security, Data Warehousing, DevOps, Dimensional Modeling, Data Flow Control, Github, Apache Hive, Identity and Access Management, Python (Programming Language), Machine Learning, Metadata, Performance Tuning, Query Optimization, Power BI, Cloud Services, Standard Sql, Azure Data Lake, SQL Databases, Data Streaming, Tableau (Software), Azure Service Bus, Data Logging, Google Cloud, Cloud Platform System, Azure Data Factory, Delivery Pipeline, Apache Spark, IT Architecture, Caching, Git, Data Lakes, Pyspark, Information Technology, Data Lineage, AWS Glue, Apache Kafka, Data Management, Machine Learning Operations, Terraform, Stream Processing, Azure Synapse Analytics, Data Pipelines, Key Vault, Jenkins, Amazon Redshift, Databricks - **Published:** September 4, 2026 - **Apply:** https://www.dice.com/job-detail/caec7d23-4a4c-47f5-994b-709100ccecfa ## About the Role * Bachelor's degree in Computer Science, Engineering, Information Technology, or related field. * 10+ years of experience in Data Engineering / Big Data / Analytics. * 4+ years of hands-on Databricks experience preferred. * Strong experience designing enterprise-scale data platforms. * Demonstrated experience leading technical projects and mentoring engineers. * Strong communication and stakeholder-management skills. ## Description We are looking for an experienced Lead Databricks Data Engineer / Databricks Architect with 10+ years of overall experience in Data Engineering and strong hands-on expertise in Databricks, Apache Spark, PySpark, SQL, Delta Lake, and cloud-based data platforms. The candidate will be responsible for designing and implementing scalable Lakehouse architectures, enterprise data pipelines, data integration solutions, governance frameworks, and high-performance analytics platforms using Databricks., * Design and develop scalable data engineering solutions using Databricks and Lakehouse architecture. * Build robust ETL/ELT pipelines using PySpark, Spark SQL, Python, and SQL. * Design and implement Bronze, Silver, and Gold/Medallion architecture. * Develop and optimize Delta Lake tables, including MERGE, schema evolution, Change Data Feed, and incremental processing. * Build batch and real-time/streaming pipelines using Structured Streaming, Auto Loader, and Lakeflow. * Develop and manage Databricks Jobs/Workflows for pipeline orchestration, scheduling, dependencies, retries, and monitoring. * Implement enterprise data governance using Unity Catalog, including access control, data lineage, auditing, catalogs, schemas, and external locations. Unity Catalog provides centralized governance, access control, lineage, and auditing across Databricks data and AI assets. * Perform Spark and Databricks performance tuning, including cluster configuration, partitioning, caching, query optimization, Photon, and workload optimization. * Design data models supporting Data Warehousing, BI, Analytics, and AI/ML workloads. * Integrate Databricks with cloud platforms such as AWS, Azure, or Google Cloud Platform. * Work with cloud services such as AWS S3, Azure ADLS Gen2, Azure Data Factory, AWS Glue, Synapse, Event Hubs/Kafka/Kinesis, as applicable. * Implement CI/CD pipelines using Git, Azure DevOps/GitHub/Jenkins and Databricks deployment capabilities. * Work with Terraform/IaC for infrastructure provisioning and automation. * Troubleshoot production pipeline failures, performance issues, data-quality problems, and Spark/cluster issues. * Establish data quality, monitoring, logging, and observability practices. * Provide technical leadership, code reviews, architecture guidance, and mentorship to junior/mid-level engineers. * Collaborate with Data Architects, Data Scientists, Business Analysts, DevOps teams, and application teams. Required Technical Skills Databricks * Databricks Lakehouse Platform * Delta Lake * Unity Catalog * Databricks Workflows/Jobs * Lakeflow / Delta Live Tables * Auto Loader * Databricks SQL * Databricks notebooks * Databricks Asset Bundles * Photon * Cluster/workload optimization Big Data * Apache Spark * PySpark * Spark SQL * Structured Streaming * Kafka * Batch and real-time data processing Programming * Python * SQL * PySpark * Scala - good to have Cloud - Strong experience in at least one * AWS: S3, Glue, EMR, Lambda, Redshift, IAM, Kinesis * Azure: ADLS Gen2, ADF, Synapse, Azure DevOps, Event Hubs, Key Vault * Google Cloud Platform: GCS, BigQuery, Dataflow, Pub/Sub Data Engineering * ETL/ELT * Data Warehousing * Dimensional Modeling * Data Lake/Lakehouse * Medallion Architecture * CDC * Data Quality * Data Governance * Metadata and Data Lineage DevOps / CI-CD * Git * Azure DevOps / GitHub * Jenkins * Terraform * CI/CD automation * Infrastructure as Code Preferred / Nice-to-Have Skills * MLflow * Databricks Machine Learning * Feature Store * Mosaic AI / GenAI * dbt * Apache Airflow * Power BI / Tableau * Delta Sharing * Lakehouse Federation * Liquid Clustering * Data security and PII masking MLflow is particularly useful if the role touches ML/AI, as Databricks supports model tracking, lifecycle management, and deployment workflows alongside governed data. ## Related Videos - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) ## Related Articles - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)