Why This Role Stands Out
This hybrid role offers an exciting opportunity to leverage your Google Cloud Platform expertise and contribute to impactful data engineering projects, with excellent flexibility for remote work. If you thrive on building innovative data solutions and possess strong GCP skills, this position is perfect for you to grow your career. Apply today to join a dynamic team and make your mark!
Quick Overview
Job Description
Job Title- Google Cloud Platform Data Engineer
Location- Remote (EST)
Hiring Mode- Contract
Must Haves:
- 3+ years of hands-on Google Cloud Platform Engineering experience
- Strong experience with:
- Dataproc
- BigQuery
- Cloud Composer
- PySpark and Python
- ETL development experience with large datasets
- Experience using Python to call APIs
- Healthcare or Insurance industry experience (REQUIRED)
- Strong communication, critical thinking, and problem-solving skills
Job Description:
The team is migrating large volumes of provider data into Google Cloud Platform and building scalable data processing solutions. The engineer will receive data from upstream teams, transform and validate the data, leverage PySpark and Python-based API integrations, and deliver processed datasets to downstream consumers.
Key Responsibilities:
- Design and develop ETL pipelines in Google Cloud Platform
- Process and transform large-scale datasets using PySpark
- Utilize Dataproc, BigQuery, and Cloud Composer
- Develop Python-based API integrations
- Support data migration and modernization initiatives
- Collaborate with upstream and downstream data teams
Important Interview Notes:
The hiring manager is focused on candidates who can clearly articulate their hands-on experience, technical contributions, and thought process. Candidates should be prepared to discuss:
- Their specific contributions to projects, not just team accomplishments
- Technical decision-making and problem-solving approaches
- Real-world experience with Google Cloud Platform, PySpark, and Python
Sample topics may include:
- Removing duplicate rows from a PySpark DataFrame while retaining the latest record
- Grouping and transforming datasets in PySpark
- ETL Design Decisions and data processing methodologies
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