Data Systems and Analytics Engineer

Arbor Research Collaborative for Health•Ann Arbor, MI

2h•$107,000 - $176,000•Remote

About The Position

The Data Systems and Analytics Engineer turns raw research and program data into trusted datasets, analyses, and live reporting for federal health research programs. The role spans the full data lifecycle, including data ingestion and preparation, analytical modeling, and presentation through dashboards and automated reports. The successful candidate combines data engineering expertise in SQL, Python or R, ETL, and cloud databases with the ability to present information clearly through dashboards, live reports, and publication-ready outputs. The position also provides technical leadership as architect for data systems and processes, guiding other engineers and analysts on design and implementation. This remote position is open to candidates physically located in Colorado, Washington D.C., Georgia, Maryland, Michigan, Minnesota, North Carolina, or Virginia. It is an exempt role, reports to the Program Director, is listed at 40 hours per week, does not supervise others, and involves minimal overnight travel of no more than 5%. The salary range is $107,000 to $176,000. Base compensation is determined by market range and geographic pay zones and may also be affected by individual factors such as job level, prior experience, skills, certifications, and education.

Requirements

Bachelor's degree in computer science, data science, statistics, informatics, or a related field.
Five years of progressively more responsible experience in data engineering, programming, or reporting development.
Advanced SQL skills, including data modeling, views, stored procedures, and query optimization in relational databases.
Proficiency in Python or R for data processing and automation; working knowledge of SAS.
Demonstrated experience developing reports and dashboards, including production dashboards or live reports in Tableau, Power BI, or a custom framework such as R Shiny or Plotly Dash.
Experience building and maintaining ETL pipelines, including data validation and error handling.
Experience with Git version control in a collaborative workflow.
Ability to translate user requirements into data models, pipelines, and report designs.
Strong attention to data accuracy and documentation.
Excellent organizational and communication skills.
Demonstrated technical leadership experience, including designing data system architecture and processes, setting technical standards, and mentoring technical staff.

Nice To Haves

Experience with cloud platforms, especially AWS (such as S3, RDS, Lambda, and Glue), and an AWS or other cloud certification.
Experience with data orchestration and transformation tools such as Airflow and dbt.
Experience with record linkage or entity resolution across administrative data sources.
Experience with health research data, such as clinical study, registry, biospecimen, or claims data, and with de-identification methods.
Familiarity with government agency data-sharing policies and federal security standards such as FISMA and FedRAMP.
Experience designing accessible Section 508 dashboards and reports.
Experience with survey data or program evaluation data.
Experience supporting federal contracts.
Experience architecting cloud data platforms, such as AWS data lake or warehouse architectures, and an AWS Solutions Architect or Data Engineer certification.
Experience with architecture documentation and modeling tools such as UML, C4, data-flow, and entity-relationship diagrams.
Experience writing and maintaining SOPs in a regulated or quality-managed environment.
Experience with search and indexing tools such as Elasticsearch or OpenSearch.
Experience with AWS security and data services such as KMS, Macie, AWS Backup, Glue, and Athena.
Non-profit experience.

Responsibilities

Design, build, and maintain automated, scheduled, and monitored ETL/ELT pipelines that ingest data from databases, files, APIs, and web platforms into cloud databases and warehouses, and deliver recurring extracts.
Clean, validate, and transform raw data into analysis-ready datasets, with data dictionaries, codebooks, and documented derivation logic; ensure accuracy through automated validation checks and independent quality control.
Keep code in version control using Git and code review; document systems, pipelines, and dashboards, including archived reproducibility packages, to support maintenance and handoff.
Protect sensitive health data through de-identification and data-minimization techniques, verify de-identification performed by others, and apply federal security requirements including access controls, encryption, and audit logging.
Build record-linkage and entity-resolution processes to join data across sources using deterministic and probabilistic matching.
Harmonize data from multiple sources to common standards and maintain metadata for search and discovery, such as with Amazon OpenSearch Service or Elasticsearch.
Design relational data models, views, and stored procedures to support analysis and reporting.
Support analysts and biostatisticians with derived variables, matched comparison datasets, and reproducible analysis files.
Present complex metrics and outcomes clearly for leadership, sponsors, and the public, selecting appropriate chart types and levels of detail.
Produce publication-ready tables and figures for manuscripts, presentations, and web content.
Design, build, and maintain interactive dashboards and visualizations in Tableau, Power BI, or custom frameworks such as R Shiny, Plotly Dash, and Streamlit to track operational and performance metrics.
Ensure dashboards and reports meet Section 508 and WCAG accessibility standards.
Gather user requirements, prototype layouts, and refine designs based on feedback.
Work with project managers, biostatisticians, data managers, and external partners to meet deliverable schedules.
Explain technical approaches and design choices to technical and non-technical audiences.
Use AI, such as Claude Code, to aid pipeline solutioning and as a component of solutions, following organizational guidance and controls.
Serve as technical lead and system architect for data platforms, designing end-to-end architecture across ingestion, storage, processing, security, and delivery, including selection of cloud services, databases, and tools.
Design and document data and process workflows and governance practices that ensure consistency, quality, and scalability across projects.
Develop, document, and maintain standard operating procedures (SOPs) for data operations, including intake, processing, validation, quality control, de-identification, release, security, and backup and recovery; review and update SOPs as systems, requirements, and policies change.
Execute data operations according to SOPs, document adherence, deviations, and corrective actions, and train staff on SOP procedures.
Lead technical planning and estimation, translate project requirements into architecture and implementation plans, and identify technical risks and mitigation strategies.
Provide technical direction, code review, and mentoring to data engineers, programmers, and analysts; coordinate with software, DevOps, and security staff on integrated solutions.
Evaluate emerging technologies and recommend improvements to data infrastructure, automation, and performance.