Senior Analytics Engineer - Data Platform
A
Auto IntegrateFleet Management
USA, CAN, MEXFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonMicrosoft SQL ServerSnowflakeTableauTerraformBigQuerydbtRedshiftLookerGitHubGitHub ActionsGitLab
Requirements
- 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.
Responsibilities
- Design and implement the systems that power reporting, operational visibility, and product analytics across the organization.
- Build and scale the internal data platform from the ground up.
- Integrate analytics directly into the platform, enabling data-driven features, reporting, and insights for customers.
- Collaborate closely with leadership, engineering, and other internal stakeholders to ensure data aligns with business needs.
- Enable self-service analytics for all team members by designing clean, intuitive data models and metrics through dbt.
- Develop and refine custom data pipelines that ingest data from operational systems to the analytics platform, handling both streaming and batch data.
- 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.
- Document best practices and coach/advise other data analysts, product managers, engineers, etc. on data modeling, SQL query optimization & reusability.
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