Databricks Engineers/Architects

C2C is not available

Must be able to pass a federal background check
Preferred Clearances:
Government Clearances: Top Secret, Secret, Public Trust, DHS  Public Trust, TS/SCI-CI Poly etc.

Work Environment
Architect, engineering, consultative in nature
Candidates could be working across multiple government end clients and projects
Remote with 25% travel
Some travel may be required

Ideal Candidate Profile
Strong Databricks experience preferred.
Consultative mind set
Candidates must be able to be processed for a public trust clearance (, clean background and clean credit)
DHS experience is a plus but not required.
Candidates should be comfortable working directly with customer environments and stakeholders.
100% remote but 30% travel as needed.
Proven experience working with Databricks in production environments.
Strong proficiency in Python and/or SQL for data engineering.
Experience with Apache Spark for distributed data processing.
Knowledge of data lakehouse architecture and best practices.
Experience with cloud data platforms (AWS S3, Azure Data Lake, or Google Cloud Platform Storage).
Hands-on experience building and managing ETL/ELT pipelines.
Strong understanding of data modeling, data quality, and performance tuning.
Familiarity with CI/CD pipelines and version control (Git).
Ability to travel, as needed, to customer sites.

Role Overview
We are seeking experienced Senior Databricks Consultants to join our team. This role involves leveraging expertise in Databricks, Python, and SQL to design, build, and manage robust data engineering solutions. The ideal candidate will have a strong background in data lakehouse architecture, cloud data platforms, and ETL/ELT pipeline development to support client objectives.

 
Key Responsibilities
Work with Databricks in production environments.
Utilize Python and/or SQL for data engineering tasks.
Apply Apache Spark for distributed data processing.
Implement data lakehouse architecture and best practices.
Build and manage ETL/ELT pipelines.
Conduct data modeling, ensure data quality, and perform performance tuning.
Use CI/CD pipelines and version control with Git.
Travel to customer sites as needed.

Required Qualifications
Technical Skills:
Proven experience with Databricks in production environments.
Strong proficiency in Python and/or SQL for data engineering.
Experience with Apache Spark for distributed data processing.
Knowledge of data lakehouse architecture.
Experience with cloud data platforms such as AWS S3, Azure Data Lake, or Google Cloud Platform Storage.
Familiarity with CI/CD pipelines and version control (Git)