Analytics Engineer

📋 Description

• Design data models for flexible querying and visualization
• Instrument ML pipelines from business requirements
• Advance automation to cut data prep time; enable analysis
• Guide Analytics Engineering roadmap, timelines, and sprints
• Own data architecture and governance standards and practices
• Lead data tools selection, implementation, optimization, and integration
• Rapidly deliver prototypes for feedback
• Train teammates on data standards, DAGs, and visualization

🎯 Requirements

• Must Have: Bachelor's in quantitative field from top-tier institution
• Must Have: 2-3+ years in data engineering
• Must Have: SQL expertise and warehousing concepts (star schemas, SCDs, ELT/ETL, MPP)
• Must Have: Experience with Spark, Kafka, Hive
• Must Have: Transform data into consistent datasets and build DAGs
• Must Have: Python, Java, or Go for data processing
• Must Have: Proficiency with Git and CI/CD practices
• Must Have: Experience with Agile workflows
• Nice to Have: MS or higher in quantitative field
• Nice to Have: Dashboards with Tableau or Looker
• Nice to Have: Deep data warehouse architecture and design
• Nice to Have: Clear docs and data stories for complex asks
• Nice to Have: Self-starter who thrives in ambiguity
• Nice to Have: Growth mindset for optimization
• Nice to Have: Team player delivering high-quality work

🎁 Benefits

• Plenty of time off to relax and recharge
• Work-from-home stipend
• Employer-paid healthcare package
• Bird ride credits