Databricks Engineer- Remote

Databricks Engineer

Location: Remote

Work Hours: Must be available to work Eastern Time (ET)

Employment Type: Contract

Experience: 7 years overall; 2 years of hands‑on Databricks experience

We are looking for an experienced Databricks Engineer / Data Engineer to join our team and play a key role in designing, developing, deploying, and optimizing modern cloud‑based data solutions.

The ideal candidate will have strong hands‑on experience with Databricks, PySpark, SQL, Delta Lake, Unity Catalog, AWS, data pipelines, streaming and batch processing, along with experience using modern Databricks capabilities such as Lakeflow Connect, Lakeflow Jobs, Spark Declarative Pipelines, and Databricks Declarative Automation Bundles (DABs).

This role requires someone who can work across the complete data engineering lifecycle, from data ingestion and pipeline development through deployment, monitoring, troubleshooting, and performance optimization.

• Design, develop, test, deploy, monitor, and optimize secure batch and streaming data pipelines.
• Build scalable data ingestion pipelines using different ingestion patterns.
• Develop data solutions using Databricks, PySpark, SQL, Delta Lake, and Unity Catalog.
• Work with Lakeflow Connect, Lakeflow Jobs, and Spark Declarative Pipelines.
• Develop and manage deployments using Databricks Declarative Automation Bundles (DABs), formerly known as Databricks Asset Bundles.
• Implement data modeling concepts within data pipelines and analytical solutions.
• Build automated data quality checks, validation rules, and pipeline expectations.
• Implement CI/CD processes for data engineering workflows.
• Use Git and related development practices for source control and deployment automation.
• Develop and maintain both real‑time/streaming and batch data processing solutions.
• Monitor production pipelines and troubleshoot failures and performance issues.
• Optimize data pipelines for scalability, reliability, and performance.
• Support data preparation, data exploration, and visualization activities.
• Collaborate with engineering and business teams to understand requirements and deliver secure, efficient data solutions.
• Document existing and future‑state data flows, architecture, and technical processes.
• Participate in Agile development processes and team activities.

• 5 years of experience designing and delivering cloud‑based data solutions.

• 2 years of hands‑on Databricks experience.

• Strong experience with:

  • Databricks
  • Lakeflow Connect
  • Lakeflow Jobs
  • Spark Declarative Pipelines
  • Databricks Declarative Automation Bundles (DABs) / Databricks Asset Bundles
  • Unity Catalog
  • Delta Lake
  • Py Spark
  • SQL

• Experience developing batch and streaming data pipelines.

• Experience with data ingestion, data transformation, and data modeling.

• Experience implementing data quality, testing, monitoring, and pipeline validation.

• Experience with Git and CI/CD.

• Strong troubleshooting and production support experience.

• Experience with AWS / Amazon Web Services is preferred.

• Experience working in an Agile environment.

• Bachelor's degree in Computer Science, Engineering, Information Technology, or a related technical discipline, or equivalent professional experience.
• Strong analytical and problem‑solving skills.
• Strong communication and collaboration skills.
• Experience working with cross‑functional engineering teams.
• Experience optimizing large‑scale data processing workloads.
• Experience documenting data architecture and data flows.

• Remote position
• Candidate must be able to work during Eastern Time (ET) business hours.
• Strong communication and collaboration skills are required for working with distributed engineering teams.

The ideal candidate is a hands‑on Databricks Data Engineer who has built production‑grade data pipelines using PySpark, SQL, Delta Lake, Unity Catalog, and AWS, and has experience with modern Databricks capabilities including Lakeflow and Declarative Automation Bundles.

Candidates should be comfortable owning data engineering work from ingestion → transformation → data quality → testing → deployment → monitoring → production troubleshooting → optimization.