Databricks Data Engineering Lead

Quantum Technologies. LLC; New York (NY); Remote; USD 84,000 - 120,000; Full time

Job summary

Quantum Technologies. LLC is seeking an experienced Databricks Data Engineering Lead for remote work from New Jersey. The role requires 8+ years in data engineering, with strong hands-on Databricks, Python, PySpark, and Delta Lake expertise.

You will guide architecture, design scalable pipelines, and mentor junior engineers across teams. Responsibilities include leading enterprise-scale data solutions, optimizing Lakehouse architectures, and delivering secure, high-performance data platforms on

Qualifications

• 8+ years of data engineering experience.
• Expertise in Databricks on AWS.
• Strong Python and PySpark skills.
• Experience with Delta Lake and Lakehouse patterns.
• Ability to lead and mentor engineers.

Responsibilities

• Lead the design, development, and implementation of enterprise-scale data engineering solutions on Databricks.
• Design and develop scalable ETL/ELT pipelines using Databricks, Python, and PySpark.
• Provide hands-on technical leadership for Databricks Genie implementations and use cases.
• Design and optimize data architectures leveraging Delta Lake and Lakehouse architectural patterns.
• Build reusable frameworks and engineering components for data ingestion, transformation, validation, and processing.
• Develop and optimize complex PySpark workloads for performance, reliability, and scalability.
• Implement data quality, governance, security, monitoring, and operational controls across data pipelines.
• Provide technical leadership through architecture reviews, design reviews, code reviews, troubleshooting, and engineering best practices.
• Mentor data engineers and provide technical guidance during development and implementation.
• Work closely with architects, business analysts, data teams, and application teams to translate requirements into technical solutions.
• Support production deployments, performance tuning, incident resolution, and continuous improvement.
• Contribute to technical documentation, solution design, deployment strategy, and engineering standards.

Skills

Technical leadership

Engineering standards

Mentoring

Architecture reviews

Tools

Databricks

Databricks Genie

Python

PySpark

Delta Lake

Lakehouse architecture

SQL

Job description

US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other

Job Title: Databricks Data Engineering Lead

Location: Remote , NJ

Duration: 12 months

Bill Rate: $74/hour (C2C)

Job Type: W-2, 1099, C2C

Client: To Be Discussed Later

Work Authorization: US Citizen, Green Card, OPT-EAD, CPT, H-1B, H4-EAD, Other

Role Summary:

We are looking for an experienced Databricks Data Engineering Lead with 8+ years of overall data engineering experience and strong hands‑on expertise in building scalable data solutions using Databricks, Python, PySpark, Delta Lake, and Databricks Genie.

The candidate will be responsible for leading the design and development of modern data engineering solutions, providing technical direction to the engineering team, and ensuring scalable, secure, high‑performing implementations. Experience working with Databricks on AWS is highly desirable.

Key Responsibilities:

• Lead the design, development, and implementation of enterprise‑scale data engineering solutions on Databricks.
• Design and develop scalable ETL/ELT pipelines using Databricks, Python, and PySpark.
• Provide hands‑on technical leadership for Databricks Genie implementations and use cases.
• Design and optimize data architectures leveraging Delta Lake and Lakehouse architectural patterns.
• Build reusable frameworks and engineering components for data ingestion, transformation, validation, and processing.
• Develop and optimize complex PySpark workloads for performance, reliability, and scalability.
• Implement data quality, governance, security, monitoring, and operational controls across data pipelines.
• Provide technical leadership through architecture reviews, design reviews, code reviews, troubleshooting, and engineering best practices.
• Mentor data engineers and provide technical guidance during development and implementation.
• Work closely with architects, business analysts, data teams, and application teams to translate requirements into technical solutions.
• Support production deployments, performance tuning, incident resolution, and continuous improvement.
• Contribute to technical documentation, solution design, deployment strategy, and engineering standards.

Mandatory Technical Skills:

• 10+ years of overall experience in Data Engineering / Data Platforms.
• Strong hands‑on experience with Databricks.
• Strong practical experience with Databricks Genie.
• Advanced programming experience in Python.
• Strong development and optimization experience using PySpark.
• Strong understanding and implementation experience with Delta Lake.
• Experience designing and implementing Lakehouse‑based data platforms.
• Strong knowledge of SQL and data modelling concepts.
• Experience developing enterprise‑grade batch and/or streaming data pipelines.
• Strong understanding of data quality, observability, performance tuning, and production support.
• Experience leading technical teams and driving engineering standards and best practices.

Desired / Preferred Skills:

• Hands‑on experience implementing Databricks workloads on AWS.
• Experience with AWS data services such as Amazon S3, AWS IAM, AWS Glue, Amazon Redshift, AWS Lambda, and Amazon CloudWatch.
• Knowledge of CI/CD and DevOps practices for data engineering solutions.
• Exposure to Databricks governance and security capabilities.
• Experience working on large‑scale data modernization or cloud migration programs.
• Experience working directly with business and customer stakeholders.

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.