Analytics Engineer

Lantern•Dallas, TX

4d•Hybrid

About The Position

The Analytics Engineer is a hands-on individual contributor within Lantern’s centralized Analytics Center of Excellence (CoE), responsible for building, testing, releasing, and maintaining shared analytical data models that enable a distributed analytics community to confidently self-serve insights. The role partners with analysts and analytics leaders across the business to identify common needs, close data gaps, and improve the reliability and consistency of analytics. This is a builder role focused on durable, reusable data products, not one-off analysis. Success is measured by data quality, adoption of certified models, smooth analytics releases, and reduced ambiguity in business reporting.

Requirements

3–6 years of experience in analytics engineering or closely related data roles.
Strong SQL skills with experience delivering production analytical data models.
Handson experience with dbt (Core or Cloud).
Experience with cloud data platforms such as Databricks or Snowflake.
Experience working with regulated data (PII/PHI).
Ability to communicate data changes and assumptions clearly to nontechnical partners.
Experience working with large datasets, performing data validation, comparison, and reconciliation tasks.
Strong proficiency in Microsoft Excel for data analysis, reconciliation, and reporting.

Nice To Haves

Experience with medallion or layered data architectures.
Exposure to automated data quality tooling (e.g., dbt tests, Soda).
Familiarity with orchestration and deployment workflows (Airflow, Azure Data Factory).
Experience supporting Finance, Operations, or Commercial analytics use cases.

Responsibilities

Design and maintain fact and dimension models optimized for dashboards, reporting, and self-service analytics in Databricks using dbt.
Explore and onboard new or external data sources, developing Bronze and Silver layer models that make data usable by analysts.
Implement approved "single source of truth" KPI logic in Silver and Gold models, ensuring accuracy, consistency, and maintainability.
Maintain clear documentation to support adoption and correct usage by the analytics community.
Apply established SDLC practices, including Gitbased version control, code reviews, and participation in QA/UAT.
Partner with Business Analysts, Finance, Operations, and Strategy teams to translate recurring analytics needs into scalable, shared models.
Act as an enablement resource for a distributed analytics community by answering questions, providing guidance, and coaching on data usage and SQL best practices.
Improve shared data assets based on analyst feedback and usage patterns rather than building one-off solutions.
Implement automated data tests (schema, freshness, and business logic) as part of regular development work.
Support and lead analytics releases, including regression testing, release documentation, release notifications, and post release validation.
Perform level1 triage of data quality and performance issues, resolving issues where possible and escalating to Data Engineering when required.
Collaborate with Data Engineering to address source data issues and identify performance optimizations.