Databricks Data Engineer and Scientist
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 2d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 6d ago
The date the source published, not the day we noticed it (2026-10-27). Last seen at its source 5h ago.
We have tracked this listing since 27 Oct 2026 (5 days). The employer's own board has carried it every time we have read it, most recently 5 hours ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
United States
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $155k–225k/yr
Middle 50% of 5309 listings that do state pay — Engineering · all levels · United States · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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BambooHR employer's own board first seen 5d ago · last seen 5h ago
The listing
Parra Consulting Group (PCG) is seeking a Databricks Data Engineer and Scientist to support a federal client’s critical enterprise Lakehouse. This position designs, builds, and maintains robust data pipelines and applies data science, AI, and analytics skills to turn raw data into actionable insights across the business.
This successful candidate will be responsible for the following:
- Design, build, and maintain robust, end-to-end data pipelines for an enterprise Lakehouse on Databricks and AWS.
- Develop and manage ETL processes with comprehensive logging and error handling to troubleshoot and resolve issues.
- Design data models within a Bronze, Silver, and Gold medallion architecture, selecting dimensional or relational approaches as appropriate.
- Analyze and decompose complex legacy SQL, rebuild pipelines and models, and compare results to validate accuracy and performance.
- Optimize pipeline performance and scalability using techniques such as partitioning, bucketing, and caching.
- Use AI-assisted coding tools to accelerate pipeline analysis, data engineering, and data modeling tasks.
- Help ensure data security and compliance, and collaborate with cross-functional teams to deliver analytics-ready data.
Required Qualifications
- Bachelor’s degree in Computer Science, Computer Engineering, Engineering, Math, Physics, or Information Systems and Technology.
- 8+ years of experience designing, developing, deploying, and managing ETL processes and data pipelines.
- Ability to obtain and maintain a Public Trust or Suitability/Fitness determination based on client requirements.
- S. citizenship required to support this federal contract.
Required Experience
- Experience designing, developing, deploying, and managing ETL processes and data pipelines with comprehensive logging and error handling.
- 3+ years of experience building, debugging, and optimizing data pipelines using Databricks notebooks, clusters, and job orchestration tools, including Delta Lake and Unity Catalog.
- 3+ years of experience writing Python and PySpark code for ETL scripts and custom transformations.
- Experience using AI-assisted coding tools, such as Claude Code or Cursor, to support data engineering and data modeling tasks.
- Experience with data modeling, including selecting dimensional or relational models, within a Bronze, Silver, and Gold medallion architecture.
- Experience using AWS services, including AWS Glue, Amazon S3, and Amazon RDS.
- Knowledge of ETL performance optimization techniques, such as partitioning, bucketing, and caching, and experience troubleshooting query performance.
- Ability to analyze and decompose complex legacy SQL, rebuild data pipelines or models, and compare results to validate accuracy and performance.