Data Engineer / QA (SS22) POST NUMBER: 482084 [Remote]

Data Engineer / QA 
DETAILS
Location : Remote
Position Type : 16M Contract
Hourly / Salary : to $75W2 (based on experience)

JOB SUMMARY
Vaco is currently seeking a Data Engineer / QA for a 16M Contract opportunity that is remote. The Data Engineer / QA will support a large-scale enterprise data modernization initiative focused on building cloud-native data pipelines, modernizing legacy ETL processes, and delivering trusted, high-quality analytical data within Snowflake. The Data Engineer / QA will develop scalable ingestion pipelines, implement transformation logic, validate enterprise datasets, and support migration efforts while ensuring data quality, reconciliation, and governance throughout the delivery lifecycle. The ideal Data Engineer / QA will possess strong Snowflake engineering experience combined with data validation, testing, and quality assurance expertise. The Data Engineer / QA will be heavily focused on modern cloud data engineering and not traditional database administration or reporting development.

• Snowflake Data Engineering – Developing Enterprise Data Pipelines / ELT Workflows / Cloud-Native Data Processing Solutions within Snowflake
• Data Ingestion / ELT Development – Designing / Developing Data Ingestion Pipelines Supporting Batch / Incremental / Near Real-Time Data Processing
• SQL Development / Optimization – Developing Complex SQL Logic / Query Optimization / Transformation Processes Supporting Enterprise Analytics
• Python Data Engineering – Building Python-Based Data Processing / Automation / Validation / Pipeline Support
• DBT Development – Developing / Maintaining DBT Models Supporting Modular Data Transformations / Testing / Documentation / Analytics Engineering Best Practices
• ETL / ELT Modernization – Modernizing Legacy ETL Workloads into Cloud-Native ELT Architectures Leveraging Snowflake / DBT
• Change Data Capture (CDC) – Developing CDC Pipelines Supporting Incremental Data Loads / Synchronization / Low-Latency Data Movement
• Data Validation / Reconciliation – Performing Source-to-Target Data Validation / Reconciliation / Completeness Verification to Ensure Accurate / Trusted / Consistent Data
• Data Quality / QA – Supporting Data Quality Validation / QA Reviews / Testing Activities Throughout Development / Migration / Production Deployment
• Migration Support – Supporting Enterprise Data Migration Initiatives (Legacy Platform Modernization / Data Conversion / Validation Activities)
• CI/CD Automation – Building Automated Deployment Pipelines Supporting Data Engineering Releases / Testing / Production Promotion
• DevOps Collaboration – Partnering with Data Engineering / Architecture / QA Teams Using Azure DevOps / Git throughout Agile Delivery

JOB REQUIREMENTS

• Snowflake – Developing Enterprise Data Pipelines / ELT Processes / Cloud Data Warehouse Solutions within Snowflake
• SQL – Strong Experience Developing Complex SQL Queries / Stored Procedures / Data Transformations / Performance Optimization
• Python – Developing Python-Based Data Engineering Solutions Supporting Automation / Pipeline Development / Data Validation
• DBT – Experience Building Modular DBT Models / Transformations / Tests / Documentation Supporting Analytics Engineering Best Practices
• ETL / ELT Development – Experience Designing / Developing / Modernizing Enterprise Data Integration Solutions
• Change Data Capture (CDC) – Experience Implementing CDC Pipelines Supporting Incremental Data Processing / Data Synchronization
• Data Validation / Reconciliation – Experience Performing Source-to-Target Validation / Data Reconciliation / Quality Verification Across Enterprise Data Pipelines
• Data Quality / QA – Experience Supporting Data Quality Validation / Testing / QA Processes Throughout Enterprise Data Delivery
• CI/CD – Experience Building Automated Deployment Pipelines Supporting Data Engineering Development and Production Releases
• Azure DevOps – Experience Managing Agile Delivery / Source Control / Build Pipelines / Release Management within Azure DevOps
• Git – Experience Utilizing Git for Source Control / Branching / Code Reviews / Collaborative Development
• Analytical Problem Solving – Investigating Data Quality Issues / Pipeline Failures / Transformation Errors / Root Cause Analysis Supporting Continuous Improvement

PREFERRED (not required)

• Data Vault – Designing / Supporting Data Vault Architectures for Scalable Enterprise Data Warehouse Solutions
• Kimball Methodology – Designing Dimensional Models (Star Schemas / Fact Tables / Conformed Dimensions) Supporting Enterprise Reporting / Business Intelligence
• Enterprise Data Governance – Supporting Enterprise Data Governance / Metadata Management / Business Glossary Initiatives
• Business Intelligence Platforms – Experience Supporting Modern BI / Reporting Platforms Including Power BI / Tableau / Looker
• Agile Data Delivery –Working in Agile Data Engineering / Analytics Delivery Teams Supporting Iterative Solution Development / Delivery

Determining compensation for this role (and others) at Vaco/Highspring depends upon a wide array of factors including but not limited to the individual’s skill sets, experience and training, licensure and certifications, office location and other geographic considerations, as well as other business and organizational needs. With that said, as required by local law in geographies that require salary range disclosure, Vaco/Highspring notes the salary range for the role is noted in this job posting. The individual may also be eligible for discretionary bonuses, and can participate in medical, dental, and vision benefits as well as the company’s 401(k) retirement plan. Additional disclaimer: Unless otherwise noted in the job description, the position Vaco/Highspring is filing for is occupied. Please note, however, that Vaco/Highspring is regularly asked to provide talent to other organizations. By submitting to this position, you are agreeing to be included in our talent pool for future hiring for similarly qualified positions. Submissions to this position are subject to the use of AI to perform preliminary candidate screenings, focused on ensuring minimum job requirements noted in the position are satisfied. Further assessment of candidates beyond this initial phase within Vaco/Highspring will be otherwise assessed by recruiters and hiring managers. Vaco/Highspring does not have knowledge of the tools used by its clients in making final hiring decisions and cannot opine on their use of AI products.