Research Application Support Engineer - Remote

American Chemical Society•Atlanta, TX

6d•$82,000 - $102,000•Remote

About The Position

The American Cancer Society (ACS) is seeking a Research Application Support Engineer to primarily support its Discovery pillar. This role is crucial for advancing research that influences government policies, provides guidance for cancer screening recommendations, and upholds ACS's reputation in the cancer research community. The engineer will be responsible for application and data installations, maintaining server environments, and assisting with user permissions for research teams. This position ensures research-specific applications and datasets are properly installed, configured, and updated, while also providing ongoing server upkeep and technical support. The role involves close collaboration with business stakeholders, IT product teams, endpoint, data, architecture, infrastructure, and application support teams to optimize data pipelines, analytical models, and reporting datasets for the Discovery pillar's specific needs. This is a remote position based anywhere within the United States.

Requirements

Bachelor's Degree in Computer Science, Engineering, or equivalent experience.
3-5 years of relevant experience.
3+ years of experience with supporting, monitoring, troubleshooting data pipelines including ETL, cloud-storage, reporting, and deployment.
Proficiency in cloud-based data engineering, preferably within Microsoft Azure, including services such as Azure Data Factory, Azure Databricks, and familiarity with other cloud platforms (AWS, GCP) a plus.
Strong experience with R / RStudio (large data model troubleshooting), SAS, SQL and Python, including data manipulation, transformation, and automation in support of scalable data pipelines.
Hands-on experience with modern data warehousing and transformation tools, including Snowflake and Dbt, for building reliable, maintainable data models.
Familiarity of CI/CD practices and version control (e.g., Git, GitHub/GitLab) as part of modern data engineering workflows, including automated testing and deployment.
Understanding of data quality and privacy, governance, and documentation best practices, ensuring accuracy, consistency, and compliance across data assets.
Excellent business relationship and communication skills, with the ability to collaborate effectively across various teams and convey complex information clearly to both technical and non-technical stakeholders.

Nice To Haves

SAS or R /R Studio experience preferred.
Experience or familiarity with healthcare or research data (e.g., clinical, claims, EHR, biospecimen, genomic, or imaging data) preferred.
Additional platforms/software where experience is considered a plus: SPSS, EndNote, JoinPoint, DevCan, ArcGIS, Origin, Streetsmart, MatLab, Social Explorer, Traveltime, Tableau, Stata.

Responsibilities

Provide advanced technical support for SaaS platforms and analytics tools, resolving data issues (including user login access problems) and ensuring seamless integration with enterprise data systems.
Collaborate with vendors and internal teams to quickly address platform, user access, and data challenges.
Provide ongoing support for scalable data pipelines that enable analytics, modeling, and reporting.
Maintain and troubleshoot data pipelines to keep analytics and reporting running smoothly.
Ensure large datasets are processed reliably, fix any issues promptly, and collaborate with teams to make data easy to use and queries run efficiently.
Advise IT team members and business stakeholders regarding alternative ways to use existing ACS technology or considerations regarding future investments.
Proactively monitor server availability to ensure systems remain operational and address any downtime or disruptions promptly.
Develop and maintain robust disaster recovery solutions to protect data integrity, minimize the impact of system failures, and ensure rapid restoration of services.
Create comprehensive technical documentation for IT support, including detailed records of data models, pipelines, server environments, and transformation workflows.
Assist business, where needed, in following ACS and industry best practices related to software development lifecycle, given their work in data science, code, and configuration.
Support and troubleshoot large data models within advanced analytics platforms and optimize execution performance of statistical and analytical workloads (runtime, memory usage) to enhance overall performance with complex data.
Diagnose errors within data pipelines and transformation logic and implement improvements that increase model scalability for large datasets.
Work collaboratively with various IT and business stakeholders to ensure reliable access to trusted data for analytics and business users.
Support the Discovery pillar by troubleshooting and resolving issues promptly.