Build reliable and governed data pipelines that improve the accuracy, consistency, accessibility, and trustworthiness of enterprise data.
Develop reusable components, libraries, templates, and automation to improve engineering productivity and solution consistency.
Implement data-quality validation, monitoring, observability, and remediation processes for critical data assets.
Integrate data solutions with governance capabilities, including metadata, lineage, classification, certification, ownership, and access controls.
Develop and maintain governed data assets supporting AI, Machine Learning, analytics, semantic models, APIs, Genie Spaces, and AI agents.
Build and maintain workflow orchestration, including scheduling, dependencies, parameters, retries, notifications, and operational monitoring.
Troubleshoot and optimize data workloads using Spark diagnostics, job logs, query plans, partitioning strategies, join optimization, and Adaptive Query Execution.
Support production data solutions through monitoring, incident resolution, performance tuning, migrations, upgrades, and cost optimization.
Participate in technical design discussions, code reviews, testing, and continuous improvement of engineering practices.
Collaborate with product owners, architects, operations teams, and other engineers to resolve technical dependencies and deliver solutions.
Maintain technical documentation for data pipelines, data models, operational procedures, and engineering solutions.
Required Qualifications
Bachelor’s degree in computer science, Engineering, Mathematics, Information Systems, or a related STEM field, or equivalent education and professional experience.
4+ years of experience in data engineering, software engineering, or a related technical discipline, including experience developing and supporting production data solutions.
Hands-on experience with Apache Spark/PySpark, Delta Lake, Python, and SQL.
Experience building and supporting production batch and/or streaming data pipelines.
Experience with Databricks capabilities such as Lakeflow Declarative Pipelines, Databricks Jobs, Auto Loader, Structured Streaming, Delta Lake, and Unity Catalog.
Experience developing ETL/ELT pipelines for ingesting, transforming, validating, and publishing enterprise data.
Experience with workflow orchestration, including scheduling, dependencies, parameters, retries, notifications, and monitoring.
Understanding of Unity Catalog and data governance concepts, including catalogs, schemas, tables, views, lineage, access controls, and permissions.
Experience with Git and CI/CD practices and exposure to Infrastructure as Code technologies such as Terraform or Databricks Asset Bundles.
Strong Python and SQL development skills with the ability to create maintainable, testable, and production-ready data solutions.
Experience troubleshooting and optimizing Spark workloads using Spark UI, job logs, query plans, partitioning, and join optimization.
Understanding of relational, dimensional, logical, and physical data modeling concepts.
Experience working across the engineering lifecycle, from requirements and development through testing, deployment, monitoring, and production support.
Strong problem-solving and analytical skills with the ability to diagnose and resolve data and production issues.
Strong written and verbal communication skills with the ability to collaborate effectively with technical and business teams.
Experience working within Agile delivery models such as Scrum, Kanban, or SAFe.
Advanced experience with Python and PySpark for data processing, automation, and reusable engineering components.
Experience with cloud platforms such as Microsoft Azure, AWS, or Google Cloud.
Experience with cloud storage technologies such as Azure Data Lake Storage Gen2, Amazon S3, OneLake, or Google Cloud Storage.
Experience with modern data technologies such as Snowflake, Apache Iceberg, Apache Hudi, or BigQuery.
Experience building ingestion pipelines using Databricks Auto Loader.
Experience developing streaming solutions using Structured Streaming
Experience designing or consuming REST APIs and web services.
Experience implementing data-quality, metadata, governance, or lineage capabilities.
Experience developing data products supporting AI, Generative AI, Machine Learning, semantic models, APIs, advanced analytics, Genie Spaces, or AI agents.
Experience working with insurance and Health domain data
Practical experience using AI-assisted development tools such as GitHub Copilot, Claude, or Databricks AI-assisted development capabilities to improve engineering productivity.
Strong experience building and supporting production-scale Databricks Lakehouse solutions.
Strong Python skills applied to reusable data engineering frameworks, automation, libraries, and utilities.
Experience working with Delta Lake and modern lakehouse architectures.
Experience developing governed datasets and data products for analytics, AI, and Generative AI use cases.
Experience with automated testing, deployment pipelines, Infrastructure as Code, and modern software engineering practices.
Demonstrated ability to troubleshoot complex data problems and improve the reliability, performance, and maintainability of existing solutions.
A strong engineering mindset with an emphasis on quality, automation, observability, scalability, and continuous improvement.
Teammate Benefits & Total Well-Being
We go beyond standard benefits, focusing on the total well-being of our teammates, including:
Health Benefits: Medical/Rx, Dental, Vision, Life Insurance, Disability Insurance
Financial Benefits: ESPP; 401k; Student Loan Assistance; Tuition Reimbursement
Mental Health & Wellness: Free Mental Health & Enhanced Advocacy Services
Beyond Benefits: Paid Time Off, Holidays, Preferred Partner Discounts and more.
Not reflective of all benefits. Enrollment waiting periods or eligibility criteria may apply to certain benefits. Benefit details and offerings may vary for subsidiary entities or in specific geographic locations.
Recruiting Vendor Disclosure Statement
Brown & Brown does not accept unsolicited resumes from external recruiters, recruitment vendors or employment agencies ("Recruiting Vendors"). Recruiting Vendors must have a valid written agreement and received prior written authorization from an authorized Brown & Brown representative before submitting candidates for any publicly posted role. Any unsolicited resumes submitted to Brown & Brown or its employees become the property of Brown & Brown, and no fees will be paid for such submissions. Additional information regarding this policy can be found on our careers page.
The Power To Be Yourself
As an Equal Opportunity Employer, we are committed to fostering an inclusive environment comprised of people from all backgrounds, with a variety of experiences and perspectives, guided by our Diversity, Inclusion & Belonging (DIB) motto, “The Power to Be Yourself”.
