Senior Analytics Engineer, Fleetio

• The ideal candidate for this role is a business-savvy senior-level analytics engineer with experience in building and owning complex analytics platforms and working closely with stakeholders
• You’re comfortable starting from a relatively open landscape and defining scalable architecture, modeling standards, and best practices
• You have a deep understanding of data modeling, data transformation, and data warehousing on modern data platforms
• Your experience goes beyond just data engineering; you have a strong foundation in analytics and have worked closely with customer-facing products
• You bring strong but adaptable opinions on design patterns, architecture, and data modeling, and you are passionate about solving business challenges by delivering scalable, data-driven solutions
• You have a knack for translating ambiguous business questions into clear, actionable data insights and incrementally deliver impactful results
• Product-minded and team-oriented, you actively listen to others’ perspectives and educate on best practices
• Excellent communication, especially written, is one of your strengths
• 5+ years of experience with a proven track record in data or analytics engineering
• Experience transforming raw data into clean models using standard tools of the modern data stack and a deep understanding of ELT and data modeling concepts
• Hands-on experience building and orchestrating ELT pipelines using Azure Data Factory
• Experience with Microsoft SQL Server
• Proficiency in Python and orchestration tooling like Prefect or Dagster
• Experience in designing, building, and administering modern data pipelines and data warehouses
• Experience with dbt
• Experience with semantic layers like Snowflake's semantic views, Cube, or Metricflow
• Experience with Snowflake, BigQuery, or Redshift
• Experience with version control tools such as GitHub or GitLab
• Experience with CI/CD and IaaC tooling such as GitHub Actions and Terraform
• Experience with business intelligence solutions (Metabase, Looker, Tableau)
• Excellent communication and project management skills with a customer service-focused mindset

Desirable

• We are looking for a Senior Analytics Engineer to play a foundational role in building Auto Integrates’ internal analytics infrastructure
• In this role, you will design and implement the systems that power reporting, operational visibility, and product analytics across the organization
• You will focus primarily on building and scaling our internal data platform from the ground up
• In close partnership with Product & Engineering, you will also help integrate analytics directly into the platform — enabling data-driven features, reporting, and insights for our customers
• You will collaborate closely with leadership, engineering, and other internal stakeholders to ensure data aligns with business needs and supports strategic objectives
• This is an opportunity to shape the foundation of Auto Integrate's analytics ecosystem, where data plays a central role in how we operate, how we grow, and how we deliver value to customers
• This position will require experience in building a data platform from the ground up, enabling the team to access analytics in ways they have not before
• Enable self-service analytics for all team members by designing clean, intuitive data models and metrics through dbt, empowering employees to make informed, data-driven decisions
• Develop and refine custom data pipelines that ingest data from operational systems to our analytics platform, handling both streaming and batch data using third-party tooling and home-grown solutions
• Maintain and optimize the data platform infrastructure, focusing on data quality, ELT efficiency, and platform hygiene
• Architect and implement key components of the analytics infrastructure, such as BI, semantic layers, and foundational data warehouse
• Develop and maintain streaming data pipelines from a variety of databases and data sources
• Collaborate across business units to understand data needs and ensure required data is collected, modeled, and available to team members
• Document best practices and coach/advise other data analysts, product managers, engineers, etc. on data modeling, SQL query optimization & reusability, etc. Keep our data platform tidy by managing roles and permissions and deprecating old projects