Key skills
SQLData warehousingBig data technologiesGeneral-purpose programmingGitAgile practicesData visualizationDocumentationTeam playerSelf-starterPythonJavaGoMachine LearningETLELTData EngineeringAnalyticsLookerTableauSparkKafkaVersion ControlAgileCI/CD
About this role
Bird is a global leader in micromobility, dedicated to shaping the future of urban movement. As an Analytics Engineer, you will lead data pipeline, data modeling, architecture and data strategy-related efforts for the Data team, working closely with cross-functional partners to drive business strategy through data solutions.
Responsibilities:
- 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
Requirements:
- 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
- MS or higher in a quantitative field (CS, Engineering, Math, Stats, Finance) from a top-tier institution
- Proven ability to build complex reports and dashboards using tools like Tableau or Looker
- Deep understanding of data warehouse architecture and data design principles
- Ability to translate complex technical asks into clear documentation and compelling data stories for any audience
- A self-starter who thrives in ambiguity and can manage projects independently from ideation to execution
- A growth mindset focused on seizing opportunities to optimize products, business processes, and team knowledge
- A dedicated team player who delivers timely, high-quality work and expects the same from their peers