About this role
Job title: Research Application Support Engineer
About the Role The Research Application Support Engineer supports the Discovery pillar at the American Cancer Society. You will install and configure research applications and datasets, maintain server environments, and assist the IAM team with access permissions. This remote role partners with business stakeholders and IT teams to optimize data pipelines, analytical models, and reporting datasets for the Discovery pillar.
What You'll Do
- Data Platform Support: Provide advanced technical support for SaaS analytics platforms; resolve data issues including login access; ensure seamless integration with enterprise data systems; collaborate with vendors and internal teams to address platform, user access, and data challenges. Examples include SAS, SAS Studio, SAS Callable Sudaan, Manifold Science Cloud, R and R Studio.
- Data Pipeline and Transformation Engineering: Maintain and troubleshoot scalable data pipelines for analytics, modeling, and reporting; ensure large datasets processed reliably; advise IT and business stakeholders regarding alternative usage or future investments.
- Data Quality, Governance, and Documentation: Monitor server availability and develop robust disaster recovery solutions; create comprehensive technical documentation for IT support including data models, pipelines, server environments, and transformation workflows; promote best practices for software development lifecycle.
- Data Modeling Troubleshooting and Support: Troubleshoot large data models; optimize performance of analytics workloads; diagnose errors in pipelines and transformations; implement scalability improvements.
- Analytics Enablement & Stakeholder Collaboration: Work with IT and business stakeholders to ensure reliable access to trusted data; troubleshoot and resolve Discovery issues promptly.
What We're Looking For
- Bachelor's degree in Computer Science, Engineering, or equivalent experience; 3-5 years relevant experience; 3+ years experience supporting, monitoring, troubleshooting data pipelines (ETL, cloud storage, reporting, deployment). SAS or R/R Studio experience preferred.
- Proficiency in cloud-based data engineering, preferably Microsoft Azure (Azure Data Factory, Azure Databricks); familiarity with AWS, GCP a plus.
- Strong experience with R/RStudio, SAS, SQL, and Python; data manipulation, transformation, and automation for scalable data pipelines.
- Experience with Snowflake and dbt; CI/CD and version control (Git, GitHub/GitLab); data quality, governance, and documentation best practices; healthcare or research data experience preferred.
- Excellent business relationship and communication skills.
Nice to Have
- Hands-on experience with Snowflake and dbt; experience with Tableau, ArcGIS, Stata, SAS Studio, and other analytics tools is a plus.
Compensation & Benefits
- Salary and benefits information not disclosed in the posting.