Data Systems and Analytics Engineer

Our core values define who we are as an organization and serve as the foundation of our code of ethics. Arbor Research expects all employees to embody and demonstrate in our day-to-day work. 

• Collaborative Spirit: We expect each staff member to learn from and teach one another and ask each to exhibit curiosity and respect for the contributions of others. 
• Creativity: We strive to cultivate a challenging, stimulating, and supportive environment where our employees are expected to be inquisitive, take the initiative, and demonstrate ingenuity. 
• Credibility: Our employees are smart, thoughtful, and objective in their work. We expect them to be technically solid in their area of expertise and for those researching to demonstrate scientific rigor. 
•  Dedication: Our team achieves success because of our dedication to carrying out high-quality work and delivering trusted results. 

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: ingesting and preparing data, modeling it for analysis, and presenting results through dashboards and automated reports.

The ideal candidate combines strong data engineering skills (SQL, Python or R, ETL and cloud databases) with the design sense to present information clearly in dashboards, live reports and publication-ready outputs. The role also provides technical leadership, serving as architect for data systems and processes and guiding other engineers and analysts on design and implementation.

• →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 data warehouses, and deliver recurring extracts.
• →Clean, validate and transform raw data into analysis-ready datasets, with data dictionaries, codebooks and documented derivation logic. Ensure the accuracy of data products through automated validation checks in pipelines and reports and independent QC.
• →Keep all code in version control (Git) with code review, and document systems, pipelines and dashboards, including archived reproducibility packages, so they can be maintained and handed off to other staff.
• →Protect sensitive health data by applying de-identification and data minimization techniques, verification of de-identification performed by others following federal security requirements (e.g., HIPP, FISMA, NIST SP 800-53), 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 data standards and maintain metadata for search and discovery (e.g., Amazon OpenSearch Service or Elasticsearch).
• →Design relational data models, views and stored procedures that support both 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, choosing the right 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 (e.g., R Shiny, Plotly Dash, Streamlit) that track operational and performance metrics, such as volumes, turnaround times, trends and utilization.
• →Ensure dashboards and reports meet Section 508 and WCAG accessibility standards.
• →Gather requirements from users, prototype layouts, and refine them based on feedback.
• →Work with project managers, biostatisticians, data managers and external partners to meet deliverable schedules.
• →Explain technical approaches and design choices to both technical and non-technical audiences.
• →Leverage AI (e.g. Claude Code) as both a tool to aid pipeline solutioning and as a component for solutions, following organizational guidance and controls.

Technical Leadership role

• →Serve as technical lead and system architect for data platforms, designing end-to-end architecture across ingestion, storage, processing, security, and delivery layers, including selection of cloud services, databases and tools.
• →Design and document data and process workflows and data governance practices that ensure consistency, quality and scalability across projects.
• →Develop, document and maintain standard operating procedures (SOPs) for data operations, including data intake, processing, validation and quality control, de-identification, release, security, and backup and recovery; review and update SOPs as systems, requirements and policies change.
• →Execute data operations in accordance with established 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, and coordinate with software, DevOps and security staff on integrated solutions.
• →Evaluate emerging technologies and recommend improvements to data infrastructure, automation and performance.

Candidates are expected to be strong in several of these areas, not all of them.

• Experience with cloud platforms, especially AWS (e.g., S3, RDS, Lambda, Glue), and an AWS or other cloud certification.
• Experience with data orchestration and transformation tools (e.g., Airflow, 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 (e.g., FISMA, 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 (e.g., AWS data lake or warehouse architectures) and an AWS Solutions Architect or Data Engineer certification.
• Experience with architecture documentation and modeling tools (e.g., 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 (e.g., Elasticsearch/OpenSearch).
• Experience with AWS security and data services (e.g., KMS, Macie, AWS Backup, Glue, Athena).
• Non-profit experience.

• Bachelor's degree in computer science, data science, statistics, informatics or a related field, with five (5) 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 (e.g., PostgreSQL, SQL Server, Amazon RDS).
• Proficiency in Python or R for data processing and automation; working knowledge of SAS.
• Demonstrated experience in report and dashboard development, including production dashboards or live reports in Tableau, Power BI or a custom framework (e.g., R Shiny, Plotly Dash).
• Experience building and maintaining ETL pipelines, including data validation and error handling.
• Experience with version control (Git) in a collaborative workflow.
• Demonstrated 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 experience providing technical leadership, including designing data system architecture and processes, setting technical standards, and mentoring technical staff.

*Base Compensation is determined by market range and geographic location pay zones. Base Compensation also includes various individual factors unique to each candidate such as job level, prior experience, skill set, certifications, and education, which may impact the compensation structure.