Databricks Engineer- Remote

Centillion Infotech LLC.; United States; Remote; USD 120,000 - 160,000; Part time

Job summary

Centillion Infotech LLC. is seeking an experienced Databricks Engineer/Data Engineer for a remote contract role. The ideal candidate has 7+ years of overall experience and at least 2 years hands-on Databricks experience, with strong PySpark, SQL, Delta Lake, and AWS skills.

The role covers the full data engineering lifecycle from ingestion to deployment, monitoring, and optimization, including CI/CD, data quality, and collaboration with cross-functional teams.

Qualifications

• 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, DABs/Databricks Asset Bundles, Unity Catalog, Delta Lake, PySpark, SQL.

Responsibilities

• Design, develop, test, deploy, monitor, and optimize secure data pipelines (batch and streaming).
• Build scalable data ingestion pipelines using different patterns.
• Develop data solutions with Databricks, PySpark, SQL, Delta Lake, Unity Catalog.
• Work with Lakeflow Connect, Lakeflow Jobs, Spark Declarative Pipelines.
• Develop and manage deployments using DABs/Databricks Asset Bundles.
• Implement data modeling within pipelines and analytics solutions.
• Create data quality checks, validation rules, and pipeline expectations.
• Implement CI/CD for data engineering workflows.
• Use Git and related practices for source control and deployment automation.
• Support real-time and batch data processing; monitor and troubleshoot production pipelines.

Skills

Hands-on data engineering

Troubleshooting

Team collaboration

Communication

Education

Bachelor's degree or equivalent in CS/Engineering/IT

Tools

Databricks

Lakeflow Connect

Lakeflow Jobs

Spark Declarative Pipelines

Databricks Declarative Automation Bundles (DABs)

Databricks Asset Bundles

Unity Catalog

Delta Lake

PySpark

SQL

AWS

Git

CI/CD

Job description

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

Job Overview

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.

Key Responsibilities

• 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.

Required Technical Skills

• 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
  • PySpark
  • 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.

Preferred Qualifications

• 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.

Work Environment

• 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.

Ideal Candidate Profile

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.