• Identify and solve high-impact data problems both independently and by driving cross-functional collaboration, translating business goals into actionable analytics solutions, and actively managing timelines to execute deliverables
• Build advanced, reliable data pipelines and infrastructure to process complex, large-scale datasets while maintaining data integrity and privacy; ensure work is fully traceable and replicable through written documentation, enabling the organization to handle increasing data volume and complexity
• Promote a culture of innovation and provide technical leadership by mentoring junior team members and identifying opportunities for and implementing novel approaches to data challenges or more robust data systems, processes, and tooling
  
  

• You love understanding business requirements and processes and what it means for data; you have solid technical foundations and data projects under your belt, love collaborating with non-technical team members as much as the data team, and can own your projects end-to-end
• You work and learn fast - you focus on solving problems with the team, even if it sometimes means going outside your paper responsibilities
• You’re excited about being in a growing startup and you’re motivated by Hive Health’s mission for accessible healthcare ?
  
  

• You’re looking for a role with very deep specialization
• You need detailed specifications & requirements provided to you to get started, rather than driving the cross-functional conversations yourself to deliver on business goals
• You love getting things right the first time more than experimentation & iteration
  
  

• Previous work experience in data analytics, analytics engineering, and/or data engineering (2-3 years)
• Solid software engineering fundamentals (Python, REST APIs, version control)
• Experience working with business intelligence/dashboarding tools (e.g. Metabase, Tableau, Power BI) and/or other data visualization frameworks on Python
• Experience using Python for data science (e.g. sklearn, pandas, numpy) and good great understanding of supervised vs unsupervised learning techniques, dimensionality reduction, evaluation metrics; strong math and statistics skills
• Proficiency in SQL, relational databases; experience creating reproducible workflows for data cleaning, transformation, modeling, & storage
• Familiarity with cloud storage and compute services(e.g. Google BigQuery, Cloud Storage or AWS S3, Cloud Engine or AWS EC2)
• Creative, detail-oriented, proactive in problem solving and identifying opportunities
• Excellent oral and written communication skills and proven ability to collaborate within a team
  
  

• Experience with ELT tools such as dbt and Airbyte; great understanding of non-relational databases
• Familiarity with workflow orchestration tools like Dagster or Airflow, and container services (Docker)
  
  

Please refer to job description.