Design and implement understandable data models that support flexible querying and data visualization
Instrument algorithms and machine learning pipelines from complex business requirements
Advance automation efforts that help the team spend less time manipulating & validating data and more time analyzing it
Guide the Analytics Engineering roadmap, communicate timelines, and manage development cycles/sprints to deliver value
Own the creation and support of internal data architecture and governance standards and best practices
Lead the selection, implementation, optimization, and integration of data tools
Rapidly deliver on concepts through prototypes that can be presented for feedback
Train fellow employees on best practices for data standards, DAGs, code, documentation and visualization and help others act as successful stewards of our internal tools

Bachelor's degree in quantitative field of study (Computer Science, Engineering, Mathematics, Statistics, Finance, etc.) from a top-tier institution
2-3+ years of relevant experience in data engineering
Expertise in writing SQL and in data-warehousing concepts such as star schemas, slowly changing dimensions, ELT/ETL, and MPP databases
Experience with big-data technologies (e.g. Spark, Kafka, Hive)
Experience in transforming flawed/changing data into consistent, trustworthy datasets, and in developing DAGs to batch-process millions of records
Experience with general-purpose programming (e.g. Python, Java, Go), dealing with a variety of data structures, algorithms, and serialization formats
Proficiency with Git (or similar version control) and CI/CD best practices
Experience in managing workflows using Agile practices

Plenty of time off to relax and recharge, plus a wellness resource to help you wind down.
A work-from-home stipend.
An employer-paid healthcare package.
Of course, Bird ride credits to get you where you need to be!

Analytics Engineer

Key skills