Analytics Engineer II

Fred Hutchinson Cancer Center is an independent, nonprofit organization providing adult cancer treatment and groundbreaking research focused on cancer and infectious diseases. Based in Seattle, Fred Hutch is the only National Cancer Institute-designated cancer center in Washington.

With a track record of global leadership in bone marrow transplantation, HIV/AIDS prevention, immunotherapy and COVID-19 vaccines, Fred Hutch has earned a reputation as one of the world's leading cancer, infectious disease and biomedical research centers. Fred Hutch operates eight clinical care sites that provide medical oncology, infusion, radiation, proton therapy and related services, and network affiliations with hospitals in five states. Together, our fully integrated research and clinical care teams seek to discover new cures to the world's deadliest diseases and make life beyond cancer a reality.

At Fred Hutch we value collaboration, compassion, determination, excellence, innovation, integrity and respect. These values are grounded in and expressed through the principles of diversity, equity and inclusion. Our mission is directly tied to the humanity, dignity and inherent value of each employee, patient, community member and supporter. Our commitment to learning across our differences and similarities make us stronger. We seek employees who bring different and innovative ways of seeing the world and solving problems. Fred Hutch is in pursuit of becoming an anti-racist organization. We are committed to ensuring that all candidates hired share our commitment to diversity, anti-racism and inclusion.

The Chief Data Officer of the Fred Hutchinson Cancer Center, Jeff Leek, established the Fred Hutch Data Science Lab (DaSL), which aims to develop and support the overall data strategy for the organization. The mission of the DaSL to ensure an effective data ecosystem at Fred Hutch by developing a modern, well documented, well implemented, overall data strategy. This should serve the needs and capabilities of those leveraging data at Fred Hutch regardless of "where they live" from the clinic to the research groups. We believe focusing on people is critical in building a strong, dynamic community united around using data to improve human health and cancer care. To do this, we aim to build a diverse, hybrid team that is inclusive, open, collaborative, and supportive both within our team and with our collaborators (find more about our values and culture). We are strong advocates for open science and reproducible research, thus develop our work in the open as much as possible. We rely heavily on written communication and continuous collaboration in order to support our hybrid team, as well as make our work as accessible as scalably and sustainably as possible by Fred Hutch staff.

The Analytics Engineer II will join the Data Science team in OCDO to support data excellence as a core value and feature of data science work at Fred Hutch. As an Analytics Engineer, you will collaborate with our data science team to design, build, and maintain multi-modal data models that integrate various data sources. Your work will support AI-driven research and translational data science use cases, helping to advance our scientific and technological goals. You will play a key role in data preparation, model optimization, and ensuring data quality to enable effective data-driven decision-making across the organization. You will be a data librarian, an organizer of Fred Hutch's data sets coming from both internal (e.g., electronic health record) and external (e.g. genomic screenings) clinical applications. At Fred Hutch, Analytics Engineers are passionate about what the data means and work with researchers, clinical leaders, and other stakeholders to understand how data is generated and how it can be used to answer data-driven questions about Fred Hutch and its patients.

Work with data engineers and data scientists to develop Fred Hutch's OMOP common data model, bringing in data from multiple clinical domains
• Develop and optimize extensions for multi-modal data models that combine structured and unstructured data, enabling AI-driven translational research and data science use cases.
• Ensure data quality, consistency, and governance standards are met throughout the data lifecycle, to create clean, transformed, curated data sets for clinical researchers using Databricks
• Use software engineering best practices to test data quality and deploy data models and transformations efficiently and accurately.
• Democratize data access and knowledge by documenting data sets as well as the code/tools used to generate them, employing automation as much as possible. This includes documenting data workflows, data models, and pipeline designs.
• Help to define and improve standards for style, maintainability, and best practices for data transformations with our Engineering team to support a seamless transition from our infrastructure to Fred Hutch data science and analytics teams.
• Develop a deep understanding of the data needs of clinician-researchers and train them to use curated, self-service data sets as well as analysis and visualization tools
• Collaborate with data scientists, analytic engineers, and software developers to understand data requirements, integrations, and optimize workflows for performance, scalability and reliability.

MINIMUM QUALIFICATIONS:

• BA/BS in the Biological or Social Sciences, Public Health, Epidemiology, Mathematics/Statistics, Computer Science, Data Science/Analytics, Information Sciences, or related field, or equivalent combination of education and experience
• 5+ years of demonstrated experience in data engineering, analytics and/or data science (M.A./M.S. = 2 years, PhD = 4+ years)

PREFERRED QUALIFICATIONS:

• Experience in communicating collaboratively with researchers about their data needs and technology expertise.
• Biomedical or clinical research experience including the use of real-world data (RWD) and healthcare data in a research setting
• Hands-on experience with data modeling/transformation/pipeline tools (e.g. dbt, Python, SQL, Apache Spark) and a modern cloud data platform (e.g. Databricks on AWS)
• Knowledge of database design and database management systems
• Experience using data analysis/visualization tools to enable self-service analytics (e.g., Tableau, Power BI, etc.)
• Experience with data governance processes related to protected health information
• Experience with data visualization and dashboard creation for data presentation and sharing to technical and non-technical audiences
• Comfortable working with git, GitHub and the command line
• Demonstrated commitment to valuing diversity and contributing to an inclusive working and learning environment
• Ability to effectively communicate asynchronously with collaborators and team staff using Slack, Teams, and other software supporting documentation and sharing used in the group.
• Ability to prioritize written documentation and communication with attention to multiple audiences and creating broad understanding across groups.
• Experience with collaboration tools (Teams, Slack, Jira, Confluence).

A statement describing your commitment and contributions toward greater diversity, equity, inclusion, and antiracism in your career or that will be made through your work at Fred Hutch is requested of all finalists.

The annual base salary range for this position is from $107,536 to $169,957, and pay offered will be based on experience and qualifications.

This position may be eligible for a sign-on bonus.

This position may be eligible for relocation assistance.

Fred Hutchinson Cancer Center offers employees a comprehensive benefits package designed to enhance health, well-being, and financial security. Benefits include medical/vision, dental, flexible spending accounts, life, disability, retirement, family life support, employee assistance program, onsite health clinic, tuition reimbursement, paid vacation (12-22 days per year), paid sick leave (12-25 days per year), paid holidays (13 days per year), paid parental leave (up to 4 weeks), and partially paid sabbatical leave (up to 6 months).