Analytics Engineer

Moser Consulting•Indianapolis, IN

7d•$100,000 - $135,000•Hybrid

About The Position

We are looking for a Senior Analytics Engineer to sit at the intersection of data engineering and analytics. You will own the transformation layer of data platforms — designing, building, and maintaining the data models that power dashboards, self-serve reporting, and AI solutions. You will work closely with data engineers and business stakeholders to turn raw data into clean, reliable, and well-documented datasets. Our ideal candidate is passionate about data modeling and has a strong understanding of why it matters, not just how it's done. You're naturally curious and eager to learn new tools, technologies, and approaches. Just as importantly, you can look past what a client or stakeholder asks for to understand what they actually need — so you can recommend and deliver the solution that serves them best.

Requirements

5+ years of experience in analytics engineering, data engineering, or a closely related role.
Experience with a cloud data warehouse, such as Snowflake, Databricks, or similar.
Strong SQL skills with the ability to write transformations that are performant, readable, and maintainable.
Deep knowledge of dimensional modeling (Kimball), including SCD handling and fact table selection across a range of analytical challenges.
Experience working with BI tools (Power BI, Tableau, Sigma, or similar) and understanding semantic/metric layers.
Ability to investigate anomalies and demonstrate problem-solving skills to resolve data issues.
Comfort using agentic AI workflows to accelerate development and problem-solving.
Demonstrated ability to document data models clearly for both technical and non-technical audiences.
Excellent communication skills and a track record of partnering with business stakeholders.

Nice To Haves

Experience using dbt or similar transformation frameworks.
Experience with GitHub or an equivalent for version control and CI/CD.
Familiarity with orchestration tools such as Airflow, Prefect, or Dagster.
Experience developing validation routines and reconciliation processes.
Python skills for data tasks (scripting, testing, lightweight transformations).
Deeper Power BI experience: A strong conceptual grasp of what logic belongs in Power BI versus the data warehouse.
Configuring and optimizing semantic models, including row-level security.
A solid command of DAX, including basic and time intelligence functions.
Achieving functional outcomes through techniques like field parameters, calculation groups, buttons and bookmarks, and drill-through.

Responsibilities

Design, build, and maintain scalable data models that serve as the foundation for analytics.
Partner with analysts and business teams to translate requirements into clean, reusable data assets.
Establish and enforce data modeling best practices, testing standards, and documentation conventions.
Implement data quality tests (schema tests, freshness checks, custom assertions) and own the resolution of data issues.
Collaborate with data engineers on ingestion pipelines and warehouse architecture decisions.
Build and maintain BI semantic layers and expose clean interfaces for reporting, dashboards, and self-serve analytics.
Review and mentor analytics engineers and analysts on SQL, modeling patterns, and tooling.
Drive the adoption of analytics engineering standards across the organization.
Contribute to the evaluation and rollout of new data tooling (e.g., orchestration, observability, BI platforms).