The Data & Analytics team mission is to enable rapid, data-informed decision-making for HUMAN.
We do so by curating a 360 degree view utilizing a mixture of proactive insights, self-service data sets, and supporting infrastructure. We’re looking for an Analytics Engineer to join our team to continue building the underlying infrastructure for our team. You will be the connective glue between Engineering, Data Analysts and our Operational teams to ensure that we have reliable, analytics ready data from across the org. In particular, you will focus on improving our ability to better manage the infrastructure costs as we scale beyond the 40T+ decisions we make per week across our platform of products.
What you'll do:
- Manage the ETL processes from disparate systems to centralize data in our analytic warehouse.
- Design, build, and maintain all infrastructure to be used day-to-day by the Data & Analytics team.
- Build analytic ready data sets for use by the team using data modeling best practices.
- As needed, partner with other technical functions in Engineering ensure alignment on tooling, infrastructure, and processes.
- Manage cost and efficiencies of pipelines built by the Analytics Engineering team and queries built by Data & Analytics.
- Focus on improving our understanding of cost data to improve insights into gross margin, contract management, and infrastructure spend.
- Partner closely with Engineering and DevOps to build in the pipelines and data sets needed by our Finance and FinOps teams to better manage our cost data.
Who you are:
- Excellent SQL skills (window functions, subqueries, CTEs, temp functions).
- Proficient in Python, R, and/ or dbt.
- Ability to work cross-functionally with both senior technical and non-technical team-members.
- Experience managing a modern data stack (dbt, Fivetran, Snowflake, Gitlab, Docker, Looker) managing large data sets (1B+ rows).
- Familiarity with cloud infrastructure costs (Snowflake, GCP, AWS) and financial metrics (gross margin, COGS).
- Comfortable working in an agile environment.
- 2 - 4 years of relevant experience in analytics and/ or analytics engineering.
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