Senior Analytics Engineer

Remedy•Summit, NJ

About The Position

Thirty Madison, now part of Remedy, is hiring a Senior Analytics Engineer for its centralized data team. The role owns a core data area—such as financial performance, operations, or growth and retention—from raw data sources through the metrics leadership uses to make decisions. The engineer builds the dbt models and semantic layer behind those metrics and serves as an analytical partner to the teams that use them. This is a high-ownership role on a lean, fast-moving team where requirements may be ambiguous and the engineer is expected to identify high-value problems proactively. The role leads a functional data domain, including its models, metrics, analysis, and roadmap. It designs and maintains silver- and gold-layer dbt models using Kimball dimensional modeling, defines governed and consistent company metrics, and stewards Hex as a BI product and semantic layer. The engineer establishes standards for testing, documentation, and data freshness; investigates discrepancies and addresses their underlying causes; partners with Marketing, Growth, Finance, Product, and Engineering; and improves upstream event taxonomy and instrumentation. The role requires communicating technical decisions, trade-offs, and results to audiences ranging from engineers to senior leadership. The data stack is Snowflake, dbt, Fivetran, and Hex. The posted base pay range is $160,000–$180,000, with potential to earn equity.

Requirements

At least 5 years of experience in analytics, analytics engineering, data engineering, or a closely related role.
Deep SQL skills in a cloud data warehouse, ideally Snowflake, including data-quality validation, anomaly investigation, and analysis.
Production experience with dbt, including modeling, testing, documentation, and project structure.
Strong data-modeling skills, including Kimball dimensional modeling (star schemas and slowly changing dimensions) and layered medallion architectures.
Business and analytical judgment to identify important questions and determine how to answer them.
Track record of leading cross-team data initiatives and gathering requirements from technical and non-technical stakeholders.
Clear, confident communication with senior leadership, including the ability to push back and reset expectations.
Comfort with ambiguity and a fast pace, with the ability to prioritize and deliver without complete specifications.
A bias for action, willingness to handle work ranging from ad-hoc analysis to urgent pipeline issues, and follow-through until work is complete.

Nice To Haves

Experience supporting Marketing, Growth, Sales, or Product teams, including attribution, paid media, funnels, or retention.
Experience owning a BI platform or semantic layer as a product, such as Hex, Looker, or dbt Semantic Layer.
Experience building semantic layers and documentation for AI and agentic workflows.
Familiarity with product analytics instrumentation and event tracking.
Background in consumer health, subscription, or e-commerce businesses.

Responsibilities

Own a functional data area, including its models, metrics, analysis, and roadmap, and act as the lead analyst and primary stakeholder contact for that domain.
Design and maintain silver- and gold-layer dbt models using Kimball dimensional modeling, including facts and dimensions, to support segmentation, targeting, automation, and analytics.
Define and operationalize governed, consistent company metrics, such as retention, funnel conversion, LTV/CAC/ROAS, and revenue.
Steward Hex as a BI product by maintaining semantic-layer health, cross-team modeling standards, and self-serve data products.
Establish standards for data testing, documentation, and freshness; lead root-cause analysis of discrepancies and improve architecture to prevent recurrence.
Translate ambiguous questions from Marketing, Growth, Finance, and Product into clear requirements and scalable data solutions, without dedicated PM support.
Communicate technical decisions, trade-offs, and results to engineers and senior leadership, managing expectations along the way.
Partner with Product and Engineering to improve event taxonomy, instrumentation quality, and consistency across app, web, and backend data.
Identify high-value data problems proactively, prioritize work, and deliver in a fast-paced environment.