Databricks Engineer ID86297

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

We are looking for a Data Engineer to build batch and streaming pipelines on Databricks using PySpark and Delta Lake. This person migrates legacy data warehouse and ETL workloads onto a governed Lakehouse, modeling a medallion architecture that powers analytics and AI use cases. Strong SQL, Python, and experience with Unity Catalog governance round out the role.

- Design, build, and operate batch and streaming data pipelines on Databricks using PySpark, Delta Lake, and Databricks Workflows.

- Model and maintain a medallion (bronze/silver/gold) architecture serving analytics, reporting, and machine learning consumers.

- Migrate legacy ETL and data warehouse workloads onto the Lakehouse with validated data parity and minimal business disruption.

- Use Claude or GitHub Copilot as a development accelerator, generating code scaffolding, writing and reviewing tests, creating documentation, and prototyping solutions.

- Write clean, well-tested Python and SQL; maintain high standards through code review and documentation.

- Optimize Spark jobs and Delta tables for performance and cost, including partitioning, clustering, caching, and cluster sizing.

- Implement data quality, lineage, and governance controls using Unity Catalog and automated validation checks.

- Debug, troubleshoot, and resolve pipeline failures, data defects, and production incidents.

- Participate in Agile or product-centric delivery practices, including sprint planning and retrospectives.

- Collaborate with DevOps, platform, and analytics engineers on observability, security, and compliance best practices.

- 4+ years of professional experience in data engineering, featuring direct expertise with Apache Spark and cloud-based data architectures.

- Strong hands-on experience building data pipelines with Databricks, Apache Spark (PySpark), and Delta Lake.

- Advanced SQL and Python, with strong data modeling skills across dimensional and Lakehouse patterns.

- Experience with streaming ingestion using Structured Streaming, Auto Loader, Kafka, or Event Hubs.

- Experience with workflow orchestration (Databricks Workflows, Airflow, or Azure Data Factory).

- Experience with legacy platform migrations, ETL modernization, or managing data hygiene when porting old systems.

- Strong problem-solving, collaboration, and communication skills, including mentoring junior engineers and explaining data concepts to non-technical stakeholders.

- Familiarity with Unity Catalog, data governance, access control, and PII handling.

- Experience with dbt or an equivalent transformation framework.

- Familiarity with secure coding standards and industry security best practices.

- Experience delivering production data platforms at scale.

- Upper-intermediate English level.

- Experience with Infrastructure as Code (IaC) using Terraform and CI/CD using Azure DevOps.

- Experience working with relational databases (specifically PostgreSQL) and data persistence concepts.

- Familiarity with logging and monitoring tools (e.g., Dynatrace, CloudWatch, Databricks system tables).

- Experience working in Agile or team-based development environments preferred.

- Professional growth: Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps.

- Competitive compensation: We match your ever-growing skills, talent, and contributions with competitive USD-based compensation and budgets for education, fitness, and team activities.

- A selection of exciting projects: Join projects with modern solutions development and top-tier clients that include Fortune 500 enterprises and leading product brands.

- Flextime: Tailor your schedule for an optimal work-life balance, by having the options of working from home and going to the office – whatever makes you the happiest and most productive.