Data Engineer-Wearables & iOT Analytics

Role Summary:

The Data Engineer, Wearables within the Data Science and Engineering team serves as a key technical professional advancing companion animal sensor analytics and data innovation capabilities. Sitting within the Data Science and Engineering team, this role owns the end-to-end data pipeline, transforming multi-platform wearable sensor data and unstructured behavioral assets into trusted, AI-ready datasets. By collaborating with cross-functional research teams and external vendors, this position enables advanced analytics, data science modeling, and data-driven product decision-making across the organization.

Responsibilities:

• Automated Pipeline Development & Orchestration: Lead the design, build, and maintenance of scalable, automated data pipelines across cloud and enterprise platforms (BigQuery, Snowflake, SAP) using Python and workflow orchestration tools to deliver clean, model-ready datasets.

• Cross-Functional Data Integration & Analytics Support: Partner with clinical, claims, and nutrition teams to integrate wearable sensor data with study datasets, harmonizing endpoints and logic to support claim substantiation and proof-of-concept analyses.

• Vendor & Infrastructure Technical Leadership: Serve as the primary technical contact for third-party device manufacturers and data vendors, managing handoff specifications, SLAs, data quality standards, and cloud storage optimization.

• Unstructured & AI-Ready Data Management: Organize complex, unstructured time-series and behavioral datasets into searchable, versioned structures, establishing data lineage, governance, and feature store assets for machine learning models.

Required Qualifications:

• Bachelor's degree in Computer Science, Engineering, Statistics, or a related discipline.

• 4+ years of relevant work experience in data engineering, data science, or analytics, including hands-on experience supporting research or clinical/claims studies or a related field.

• Demonstrated experience in data analysis, data cleansing, data integration and manipulation, building, operating, and troubleshooting data pipelines and analytics workflows in cloud environments (preferably GCP) using SQL and Python or R.

• Experience in either of these” working with time-series or sensor data and/or complex, multi-source study data (e.g., clinical, claims, nutrition and other related data).
  
  

Preferred Qualifications:

• Master's degree or PhD in a quantitative or science-related field.

• 2+ years of experience leading data or analytics projects, including prioritization and cross-functional stakeholder management.

• Professional experience within animal health, human health, or related scientific domains including exposure to clinical, claims or behavioral studies.

• Technical proficiency with BigQuery, Snowflake, SAP, orchestration tools (Airflow, Cloud Composer, dbt), and version control (Git).

• Demonstrated interest and experience in new tools, technologies, and apps, particularly in data, AI/ML, and IoT contexts.

• Experience with BigQuery, Snowflake, SAP, and GCP data and analytics services.

• Experience with orchestration tools (e.g., Airflow, Cloud Composer, dbt) and version control (e.g., Git).

• Strong communication skills with a proven ability to translate complex data engineering concepts for non-technical partners.

• Creative, collaborative thinker with the ability to rapidly learn new concepts, assess complex data problems, and identify proactive, pragmatic solutions.

• Demonstrated leadership skills in working with cross-functional teams and external partners.

• Able to work independently with minimal supervision, while effectively managing multiple concurrent projects.