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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Analytics Engineer/GCP/Bigquery/Chicago Local - **Company:** Kelly Services Inc. - **Location:** Chicago, IL, United States - **Salary:** $125,000.0 - $140,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Automation of Tests, Microsoft Azure, BigQuery, Customer Data Management, Information Engineering, Extract Transform Load (ETL), Data Structures, Python (Programming Language), Machine Learning, Regression Testing, Cloud Services, Standard Sql, SAS (Software), SQL Databases, Cloud Platform System, Information Technology, Cloud Migration, Data Pipelines - **Published:** July 10, 2026 - **Apply:** https://www.chicagocareersite.com/job.asp?id=3316034262&tx=HT7872THT&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * Bachelor's Degree in a quantitative or technical field (Computer Science, Data Science, Statistics, Mathematics, Engineering, or related) * Experience with analytical programming languages such as SAS 9.4, SAS Viya, Python, and SQL * Experience with cloud data platforms (GCP, AWS, or Azure) or supporting on-prem to cloud migrations * Experience with Bigquery * Experience validating data outputs, dashboards, and statistical or machine learning models * Strong understanding of data structures, ETL processes, and analytical workflows * Experience troubleshooting data discrepancies and performing root cause analysis * Ability to work across cross-functional teams, including data engineering, analytics, and business stakeholders * Strong attention to detail and commitment to data accuracy and quality Desired Skills & Experience * Experience migrating SAS-based analytical environments to cloud platforms * Experience validating and deploying machine learning models in cloud environments (e.g., Vertex AI) * Familiarity with automated testing frameworks and data pipeline orchestration tools (e.g., Airflow, Cloud Composer) * Experience optimizing analytical code and queries for performance and scalability in the cloud * Experience supporting large-scale analytics or CRM data ecosystems * Strong documentation and process design skills to support repeatable migration frameworks * Ability to translate technical findings into clear insights for non-technical stakeholders * Experience in large enterprise or highly regulated data environments ## Description Our client is seeking an Analytics Migration Engineer to join their team on a full-time basis. In this role, you will lead the migration and modernization of analytical code, data pipelines, and statistical models from legacy on-premise environments to Bitquery in modern cloud platforms (GCP), ensuring analytical continuity as outputs, data, and AI/ML models are validated across environments. You will work across data engineering, analytics, and data science teams to maintain business-critical reporting and modeling while enabling a scalable, cloud-based analytics ecosystem. This role blends migration execution, validation, and optimization, giving you the chance to help shape a modern, future-ready analytics environment., * Migrate legacy analytical code (SAS, SQL, Python) and data pipelines from on-prem environments to GCP, refactoring workflows for modern cloud architecture and best practices * Validate and reconcile outputs between legacy and cloud environments to ensure consistency and accuracy across data, reporting, and models * Perform regression testing and quality assurance across datasets, dashboards, and statistical/ML models to confirm functional parity post-migration * Support migration and re-platforming of AI/ML and statistical models, troubleshooting discrepancies in data, code logic, and performance * Partner with data engineering, analytics, and business teams to maintain continuity of business-critical reporting during migration * Build automated testing, monitoring, and validation processes to ensure long-term data and model integrity * Document migration processes, code changes, and best practices, and contribute to ongoing optimization of analytics workflows in the cloud ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [How building an industry DBMS differs from building a research one](https://www.wearedevelopers.com/videos/768-how-building-an-industry-dbms-differs-from-building-a-research-one) - [Making Data Warehouses fast. 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