Databricks Data Engineer

DevIQ•Denver, CO

2d•$120,000 - $140,000•Hybrid

About The Position

We are seeking a Databricks Data Engineer to join our growing team. In this role, you’ll build robust and scalable data pipelines on the Databricks Lakehouse Platform that support high-quality analytics and machine learning solutions for our clients. You’ll play a critical role in designing workflows that ingest, transform, clean, normalize, and augment data from various sources — leveraging Databricks Delta Lake, Unity Catalog, and Spark for production-grade pipelines. You should be comfortable working in cloud-based environments — particularly Databricks — and have experience with structured and unstructured data. A strong command of SQL and Python is essential, along with hands-on experience using Databricks (Delta Lake, Spark notebooks, Unity Catalog), Pandas, Apache Spark, or similar data transformation frameworks.

Requirements

3+ years of hands-on production Databricks experience with SQL and Python in data engineering roles
Experience with data transformation tools/frameworks such as DLT, dbt, and Apache Spark within the Databricks ecosystem
Hands-on experience with Databricks, including Delta Lake, Spark notebooks, and Unity Catalog in production environments
Familiarity with cloud data platforms (e.g., Azure, AWS)
Solid understanding of ETL/ELT processes and pipeline orchestration
Experience with DataOps processes and tools such as Databricks Asset Bundles (DABs) and using version control systems like Git
Strong communication and problem-solving skills
Proactive in seeking opportunities to learn and grow skillset
Prior experience in a consulting or client-facing role
Must be a U.S. citizen or eligible to work in the U.S.

Nice To Haves

Advanced Databricks experience including Databricks Workflows/Jobs, Lakeflow Connect, Delta Live Tables (DLT), and performance tuning at scale
Experience with modern Data and ML workflow tools (e.g., Airflow, MLflow)
Familiarity with data visualization libraries or tools (e.g., matplotlib, Power BI, Tableau)
Understanding of best practices in data modeling, data quality, and pipeline monitoring

Responsibilities

Design and build reliable, scalable data pipelines for ingestion, transformation, normalization, and augmentation
Work with structured, semi-structured, and unstructured data across diverse environments
Develop approaches to source data from diverse platforms (flat files, databases, APIs, streams, etc.)
Develop and maintain data transformation workflows in Databricks using Delta Lake, Spark notebooks, and Unity Catalog
Collaborate with cross-functional teams to gather data requirements and translate them into actionable data models
Perform data quality checks and implement validation logic to ensure reliable outputs
Create basic visualizations to support validation, exploration, and stakeholder communication
Optimize performance of pipelines in cloud and distributed computing environments