Transform, test, deploy and document data to deliver clean and trustworthy data for analysis to end-users.
Collaborate with data scientists, data analysts, and business analysts to identify the most elegant and effective data structures to understand our constantly growing and evolving business.
Help bring engineering best practices (reliability, modularity, test coverage, documentation) to our DAG and to our Data team generally.
Collaborate with data engineers to build robust, tested, scalable ELT pipelines.
On a daily basis
Data modeling: model raw data into clean, tested and reusable datasets to represent our key business data concepts. Define the rules and requirements for the formats and attributes of data.
Data transformation: build our data lakehouse by transforming raw data into meaningful, useful data elements through joining, filtering and aggregating source data.
Data documentation: create and maintain data documentation including data definitions and understandable data descriptions to enable broad-scale understanding of use of data.
Employ engineering best practices to write code and improve our code base.
What will help you succeed Must-haves
3+ years as an Analytics Engineer or equivalent role (we know titles aren t everything!). Experience with dbt is strongly preferred.
5+ years, cumulatively, in the data space (data engineering, data science, analytics, or similar)
Strong understanding of conceptual data modeling and data mart design.
An understanding of data structures and/or database design plus deep experience with SQL and Python
Experience building data pipelines and database management including Snowflake or similar
Ability to bring a customer-oriented and empathetic approach to understanding how data is used to drive the business
Strong communication skills
Nice-to-haves
Degree in math, statistics, engineering, computer science, or related technical field
Experience in predictive modeling and statistical analysis