Analytics Engineer, Bird

• 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

Desirable

• The Data Team at Bird is an integral component of every facet of the business from strategic initiatives to tactical effectiveness, and is a key resource in developing Bird's product and processes
• As an Analytics Engineer, you will lead data pipeline, data modeling, architecture and data strategy-related efforts for the Data team at Bird working in close partnership with the engineering team
• You’re a seasoned analytics professional and engineer who not only understands how to use big data in answering complex business questions but also how to design semantic layers to best support self-service reporting and discovery
• You will manage projects from requirements gathering to planning to implementation of full-stack data solutions
• You will work closely with cross-functional partners such as engineering, ops and finance to ensure that business logic is properly represented in the semantic layer and production environments, where it can be used by the wider Bird team to drive business strategy
• 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