The Role
Design and operate batch and streaming pipelines on Databricks using PySpark and Delta Lake. Modernize legacy warehouses into governed Lakehouse architectures, optimize Spark workloads, implement data quality and lineage controls, troubleshoot production incidents, and collaborate with DevOps and analytics teams. Write tested Python and SQL, support Agile delivery, mentor junior engineers, and use AI coding tools to accelerate development.
Summary Generated by Built In
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.
WHY JOIN US
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!
ABOUT THE ROLE
We are looking for an experienced Senior Data Engineer to help modernize a 15-year-old data warehouse into a governed Databricks Lakehouse. You will build batch and streaming pipelines with PySpark and Delta Lake, following a medallion architecture across bronze, silver, and gold layers. This role also uses AI tools like Claude and GitHub Copilot to speed up development.
WHAT YOU WILL DO
- 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.
MUST HAVES
- 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.
NICE TO HAVES
- 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.
PERKS AND BENEFITS
- Growth without limits: build your skills through mentorship, internal TechTalks, challenging projects, and a dedicated annual learning budget
- Competitive compensation: get recognition that reflects your skills and impact, with regular performance and compensation reviews
- Flexibility: work 100% remotely with flexible hours that support focus, autonomy, and a healthy work rhythm
- Meaningful, modern projects: build impactful products using modern technologies alongside global teams and leading brands
- Collaborative culture: join a supportive environment with zero micromanagement where ideas are welcomed and contributions are recognized
- Well-being & support: access local well-being programs and people-focused support tailored to your location
Skills Required
- 4+ years of professional data engineering experience with Apache Spark and cloud-based data architectures
- Hands-on experience with Databricks, Apache Spark or PySpark, and Delta Lake
- Advanced SQL and Python skills with data modeling experience across dimensional and Lakehouse patterns
- Streaming ingestion experience with Structured Streaming, Auto Loader, Kafka, or Event Hubs
- Workflow orchestration experience with Databricks Workflows, Airflow, or Azure Data Factory
- Experience with legacy platform migrations, ETL modernization, or data hygiene during system migrations
- Strong problem-solving, collaboration, communication, mentoring, and stakeholder communication skills
- 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 proficiency
- Infrastructure as Code experience using Terraform
- CI/CD experience using Azure DevOps
- Experience with relational databases, specifically PostgreSQL, and data persistence concepts
- Familiarity with logging and monitoring tools such as Dynatrace, CloudWatch, or Databricks system tables
- Experience working in Agile or team-based development environments
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The Company
What We Do
AgileEngine is a privately held company established in 2010 that builds dedicated teams of designers and developers. We turn good ideas into awesome software that people actually want to use. Some of the biggest names and the hottest startups around the world chose us to build their tech.
