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Confido

Data Analytics Engineer

NYC Office$170k–$210kfulltimemidAdded 1 month ago

About this role

Confido, an AI infrastructure platform for CPG brands, seeks an Analytics Engineer to design and operate a centralized data warehouse that powers reporting, dashboards, and customer insights. You'll establish data standards, unify fragmented data models across teams, and enable self-service analytics for the organization.

What you'll do

  • Design and maintain centralized data warehouse and analytics infrastructure
  • Develop scalable data models for reporting, dashboards, and customer-facing exports
  • Establish data governance, documentation, and monitoring standards across the organization
  • Consolidate and standardize data models to create a single source of truth
  • Build ETL/ELT pipelines and optimize large-scale data workloads
  • Partner with Engineering, Product, Operations, and Customer teams to define metrics and deliver insights

What they're looking for

  • SQL and data modeling expertise
  • Data warehouse design and operation (Snowflake experience preferred)
  • ETL/ELT pipeline development and orchestration
  • Distributed data processing and optimization
  • dbt or similar modern analytics tools
  • Data governance and metadata management
  • Cross-functional collaboration and communication
  • 4-8 years of relevant experience

Benefits

  • Equity compensation
  • Paid relocation to NYC office
  • Unlimited PTO
  • 401(k) through Vestwell
  • Fully paid health, dental, and vision insurance
  • MacBook provided, catered lunches, free team dinners, and unlimited snacks
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Confido

Confido builds an AI-powered financial infrastructure platform that streamlines financial operations and workflows for CPG brands. The company is hiring software engineers, forward-deployed engineers, analytics engineers, and GTM engineers to develop core product systems, customer integrations, data infrastructure, and go-to-market automation.

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Likely interview questions

  • Walk us through your experience designing and building a data warehouse from scratch. What technologies did you use, and how did you approach scaling it as the organization grew?
  • Tell us about a time you had to consolidate fragmented data models across multiple teams or products. What challenges did you face and how did you establish a single source of truth?
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