Data Engineer - AWS/Databricks - REMOTE at Jobgether

, , United States -

Full Time

Start Date

Immediate

Expiry Date

05 Apr, 26

Salary

0.0

Posted On

05 Jan, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, AWS, Databricks, PySpark, Delta Lake, ETL, ELT, Spark SQL, DataFrame APIs, Data Warehouse, Cloud-native Solutions, Data Quality, Metadata Tagging, Agile Analytics, Data Processing, Version Control, CI/CD

Industry

Internet Marketplace Platforms

Description

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Data Engineer - AWS/Databricks. In this role, you will leverage your expertise in building and maintaining scalable data pipelines and cloud-native solutions. You will play a critical role in optimizing data platforms for various federal clients, ensuring robust data integration and efficient workflows. Your contributions will significantly impact the enterprise's data modernization efforts, fostering effective data enablement and decision-making across teams. The ideal candidate will have a passion for innovation and a drive for excellence in data engineering. \n Accountabilities Build and maintain scalable PySpark-based data pipelines in Databricks notebooks. Design and implement Delta Lake tables optimized for ACID compliance and query performance. Develop robust ETL and ELT workflows integrating multiple source systems into a data warehouse architecture. Leverage Spark SQL and DataFrame APIs to implement business rules and aggregation logic. Collaborate with data architects to implement cloud-native data solutions on AWS. Optimize pipeline performance through intelligent partitioning and adaptive query tuning. Deploy and version data engineering assets using Git-integrated workflows and CI/CD tools. Monitor pipeline health and optimize cost-performance tradeoffs. Conduct technical discovery of legacy systems and design end-to-end data flows. Implement governance practices including metadata tagging and data quality validation. Requirements 10+ years of experience in data engineering and Agile analytics. 5+ years of experience architecting and maintaining complex data products at enterprise scale. 3+ years of experience with structured and unstructured data processing. 3+ years of experience building scalable ETL and ELT workflows. 3+ years of experience with AWS and Databricks for enterprise data solutions. Experience with data quality and validation frameworks. BA or BS degree. US Citizen with ability to obtain and maintain US Suitability. Benefits Innovative excellence in a recognized workplace. Competitive compensation and benefits packages. Opportunities for personal growth through tailored training and mentorship. Exceptional customer feedback and visibility in the industry. A collaborative culture that encourages teamwork. Commitment to diversity and inclusion in the workplace. \n Why Apply Through Jobgether? We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

Responsibilities

The Data Engineer will build and maintain scalable data pipelines and cloud-native solutions, optimizing data platforms for federal clients. This role involves developing ETL and ELT workflows and collaborating with data architects to implement effective data solutions.