Analytics Engineering, Sr. Manager

Lantern•Dallas, TX

4h•Hybrid

About The Position

The Senior Manager of Analytics Engineering leads the delivery of a trusted, scalable analytics foundation that enables analytics teams across Lantern to confidently self-serve insights. This role owns the quality, consistency, and adoption of enterprise data models in a Databricks Lakehouse, ensuring data is reliable, performant, and aligned to business decision-making. This is a hands-on leadership role for a builder who enjoys shaping data architecture, defining analytical products, and developing high-performing teams. Success is measured by data trust, metric alignment, and business impact, making this role ideal for a leader who thrives at the intersection of analytics, engineering, and cross-functional collaboration. Location: Dallas, TX - Hybrid schedule (3x in office per week)

Requirements

7+ years of experience in analytics engineering, data engineering, or data architecture roles.
3+ years of people leadership or team management experience.
Deep expertise with cloud data platforms (Databricks, Snowflake), dbt (core or Cloud), and SQL-based data modeling.
Strong understanding of working with and protecting sensitive data (PII, PHI, Healthcare) while also allowing analytics use cases to operate without friction.
Proven ability to work cross-functionally with analysts in operations, marketing, finance, and strategy teams to develop high-quality data for analytics.
Strong communication skills with the ability to bridge technical and nontechnical stakeholders.

Nice To Haves

Familiarity with modern data governance frameworks and metadata management tools.
Hands-on experience implementing automated data quality at scale (Soda preferred).
Experience with orchestration tools (e.g., Azure Data Factory, Airflow) and CI/CD pipelines (GitHub Actions, Azure DevOps).
Knowledge of HIPAA compliance, PHI handling, and healthcare regulatory considerations.

Responsibilities

Enterprise Data Modeling & Analytics Architecture
Design, build, and evolve enterprise data warehouse models using medallion architecture (Bronze, Silver, Gold) with SQL and dbt, balancing normalized and denormalized structures to ensure data reliability, consistency, performance, and usability.
Define and deliver the data warehouse as a product, aligning model design, availability, and performance to the needs of business stakeholders.
Develop and maintain data architecture within the Databricks Lakehouse, leveraging external tables, streaming tables, views, materialized views, and volumes to support analytical and operational use cases.
Partner with business stakeholders to define business logic and KPIs, translating requirements into Gold-layer models that support trusted, self-service analysis.
Data Quality, Testing & Reliability
Establish and maintain a robust testing framework to validate new models and perform regression testing on changes, preventing downstream data issues.
Develop and enforce data quality tests to ensure accuracy, completeness, and freshness; detect data drift; and identify data quality issues requiring stewardship intervention.
Ensure analytical data consistently meets defined standards so Finance, Strategy, and Operations can rely on auditable, trusted data.
Cross-Functional Partnership & Stakeholder Alignment
Work closely with Finance, Strategy, Commercial, and Operations leaders to ensure shared definitions, aligned KPIs, and transparent assumptions across reporting and analytics.
Act as a trusted partner in shaping analytical requirements, reducing friction between technical and non-technical stakeholders, and enabling confident self-service.
Measure and provide monthly KPI updates to department leadership, communicating progress, risks, and opportunities clearly.
Team Leadership & Delivery Management
Lead, mentor, and develop a team of Analytics Engineers who support and enable Business Analysts across the organization.
Manage the data needs backlog, prioritizing work to ensure timely delivery of high-value data assets while adapting to evolving business priorities.
Foster strong ownership, technical excellence, and continuous growth through clear expectations, coaching, and feedback.
Performance Optimization & Platform Excellence
In partnership with Data Engineering, monitor query performance and data pipeline behavior, making recommendations to improve models, data flows, and queries to meet defined SLAs.
Develop AI-friendly data assets and tools within Unity Catalog to enable Databricks agents and emerging AI-driven analytics use cases.
Technical Governance & SDLC
Ensure strong SDLC practices, including version control, code review, CI/CD, and safe deployment of production analytics models.
Maintain secure, compliant, and well-governed analytical data, particularly when working with sensitive healthcare data (PII/PHI), while minimizing friction for analytics use cases.