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Senior Analytics Engineer

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Location

Fort Wayne, IN

Posted

Today

As a Senior Analytics Engineer, you will serve as the technical leader responsible for building and maintaining the transformation and semantic layer that powers enterprise-wide analytics at PTR. Leveraging our modern data stack—including Fivetran, Snowflake, dbt, and Sigma—you will tackle complex data modeling challenges, establish scalable data models, and build trusted, high-performance datasets that drive business decision-making.

Working closely with Data Engineering, Data Architecture, and the BI team, you will translate business requirements into governed, reusable data models while defining best practices for modeling, testing, documentation, and data quality. As the technical expert for the dbt and Sigma ecosystem, you will mentor other engineers, promote engineering excellence, and ensure the analytics layer is reliable, scalable, and easy to consume.

This role bridges the gap between data engineering and business intelligence. Your primary focus is delivering clean, trusted, and well-governed datasets that enable the BI team to create impactful dashboards and insights across the organization.

Responsibilities

  • Core modeling and semantic layer: Design and build the core dbt models and Sigma datasets/semantic models the business relies on, applying dimensional modeling standards and software engineering best practices (modularity, version control, code review).
  • Enterprise shared dimensions: Own the enterprise shared dimensions — customer hierarchy, GL hierarchy, time dimensions, fiscal calendars, territory mappings, and other conformed dimensions used across Finance, Commercial, and Fleet/Ops.
  • Performance and optimization: Tune model and warehouse performance in Snowflake; optimize SQL transformations and manage compute/cost efficiency as data volume grows.
  • Testing, quality and observability: Establish and enforce testing and documentation standards in dbt; partner with the team on data quality and observability practices to ensure trust in all published data assets.
  • Mentorship and standards: Mentor Analytics Engineer I / junior engineers, review their code, and set the modeling patterns and conventions others follow (naming, layering, environment strategy).
  • BI partnership: Partner directly with the BI analyst team to translate dashboard and reporting requirements into robust dataset designs that enable reliable self-service analytics.
  • Documentation and versioning: Maintain clear documentation of models, datasets, and transformation logic using version control and CI/CD practices so BI Analysts can self-serve.
  • Continuous improvement: Identify opportunities to automate manual processes, reduce technical debt, and improve the scalability and reliability of the analytics layer.

Must have

  • Bachelor’s degree in computer science, analytics, or a related field (or equivalent work experience).
  • 5+ years of experience in analytics engineering, data engineering, or ELT/data modeling, including demonstrated senior-level ownership of a transformation layer.
  • Advanced, production-level expertise with dbt for building, testing, and documenting models.
  • Strong SQL proficiency for performance-tuned querying, transformation, and optimization.
  • Deep experience with a modern cloud data warehouse (Snowflake strongly preferred).
  • Proven expertise in dimensional/data model (star and snowflake schemas, conformed dimension, and SCDs) and semantic-layer design.
  • Experience with Fivetran (or similar ELT/ingestion tooling) and workflow orchestrations such as Astronomer.
  • Hands-on experience with version control and CI/CD practices for data workflows.
  • Demonstrated ability to mentor engineers, lead design/code reviews, and set technical standards.
  • Strong communication and collaboration skills across technical and non-technical stakeholders.
  • Ability to develop strong business acumen across multiple business units.

Nice to have

  • Hands-on experience with Sigma and semantic modeling.
  • Experience with data observability tools.
  • Python for scripting, automation, or light transformation logic.
  • Understanding of data governance, metadata management, and data privacy/compliance standards.

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