Data Engineer; Tableau & GCP ; Raleigh, NC OR Scotts Valley, CA
Job in
Raleigh, Wake County, North Carolina, 27601, USA
Listed on 2026-08-28
Listing for:
Pivotal Solutions
Full Time
position Listed on 2026-08-28
Job specializations:
-
IT/Tech
Data Engineering, Data Analyst, Data Warehousing, Data Science Manager
Job Description & How to Apply Below
We are seeking a Data Engineer (BI-focused) to join our lean Data and Intelligence team and help build a cloud-based data platform that serves as a single source of truth for business operations. In this role, you’ll have end-to-end ownership of the data lifecycle—from ingestion and transformation to modeling and visualization—helping unify data across systems and deliver reliable, timely insights that drive business decisions.
Key Responsibilities- Design, build, and maintain scalable, secure, and high-quality ELT data pipelines
- Implement data validation and quality assurance processes to ensure data integrity
- Perform exploratory data analysis and leverage built-in ML tools (e.g., Big Query ML, Vertex AI) for forecasting and anomaly detection
- Develop and maintain BI dashboards and reporting solutions to support data-driven decision-making
- Collaborate with cross-functional teams to deliver impactful, data-driven solutions
Education:
- Bachelor’s degree in Computer Science, Engineering, or a related quantitative field
- 5+ years in data engineering
- 5+ years working with relational databases and SQL
- 3+ years working with cloud-based data platforms (GCP preferred)
- Cloud: GCP; AWS (nice to have)
- Data Warehousing: Big Query (primary); SQL Server, Snowflake
- Databases: MSSQL, MySQL
- Programming: Python, SQL
- BI Tools: Tableau (required);
Looker or Power BI (nice to have) - Other: Airflow, dbt, real-time data streaming, ELT tools (e.g., Fivetran, Airbyte, Stitch), Git Hub, CI/CD pipelines, APIs
- Problem-solving:
Strong analytical, debugging, and research skills to resolve process issues or data inconsistencies - Ownership:
End-to-end ownership of data initiatives - Collaboration:
Ability to work effectively with both technical and non-technical stakeholders - Accountability:
Commitment to delivering accurate, reliable, and timely data products - Team velocity:
Focus on improving development efficiency and accelerating deployment of data models
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