Senior Data Analyst

Tillster•Charlotte, NC

14d•$115,000 - $145,000•Hybrid

About The Position

Tillster is seeking a Senior Data Analyst to join their team. This role involves owning the end-to-end delivery of complex analytical initiatives, supporting ad-hoc analysis, and acting as a trusted analytics advisor to various departments. The Senior Data Analyst will be responsible for designing and supporting A/B test experiments, acting as a Google Analytics subject matter expert, and developing high-performing dashboards and LookML in Looker. The role also includes expanding self-service analytics capabilities, ensuring data quality, writing performant SQL, and collaborating with Data Engineers and Analytics Engineers. Additionally, the Senior Data Analyst will own coding standards, mentor junior analysts, build reusable assets, and apply AI/LLM tools to accelerate analytics workflows and develop self-serve analytics agents. This is a hybrid role requiring work within Eastern Standard Time (EST).

Requirements

SQL & Data Analysis - 4-5 years
Advanced SQL (window functions, CTEs, query optimization)
Complex analytical queries and performance tuning
Business Intelligence Tools - 3-4 years
Looker expert (LookML development, Explores, dashboards)
Data visualization and self-service analytics design
Google Analytics - 2-3 years
GA4 implementation, configuration, and reporting
Tracking strategy and warehouse integration
Python/Programming - 1-2 years (preferred)
Data manipulation and automation
Version control (Git) and code review
Statistical & Analytical Skills (3-5 years)
A/B Testing & Experimentation - 2-3 years
Experimental design and statistical testing
Hypothesis formulation and results interpretation
Statistical Analysis - 3-4 years
Descriptive and inferential statistics
Trend analysis and anomaly detection
AI & Emerging Technologies (1-2 years)
AI-Augmented Analytics - 1-2 years
Hands-on LLM usage (GPT-4, Gemini, Cortex Analyst)
AI agent development and prompt engineering
AI output validation and quality standards
Business & Communication Skills (4-6 years)
Stakeholder Management - 4-5 years
Cross-functional collaboration (Marketing, Product, Finance, Engineering)
Executive communication and presentation
Trusted advisor relationships
Business Analytics - 3-4 years
Customer analytics (segmentation, LTV, churn)
KPI development and metric definition
Translating business questions to analytical frameworks
Leadership & Mentorship (2-3 years)
Team Leadership - 2-3 years
Mentoring junior analysts
Code review and standards enforcement
Building team best practices and documentation
Project Management - 3-4 years
End-to-end analytical project ownership
Managing multiple concurrent initiatives
Proactive insight delivery

Nice To Haves

Python/Programming - 1-2 years (preferred)
Data manipulation and automation
Version control (Git) and code review

Responsibilities

Own the end-to-end delivery of complex analytical initiatives — from framing the business question and scoping the analysis through execution, presentation, and follow-through on stakeholder action.
Proactively surface KPI trends, anomalies, opportunities, and risks before stakeholders encounter them.
Support ad-hoc analysis for Tillster customers, executives, and internal stakeholders — going beyond data retrieval to provide interpretive framing and actionable recommendations with a clear point of view.
Operate as a trusted analytics advisor to Marketing, Product, Finance, Engineering, Account Management, and Support/Operations.
Challenge assumptions in business requests where data tells a different story.
Translate complex findings into decision-relevant narratives.
Design and support A/B test experiments with statistically valid methodology — including hypothesis definition, sample sizing, success and guardrail metric selection, and results analysis.
Communicate findings clearly to Product and Marketing stakeholders.
Act as the team's primary Google Analytics subject matter expert.
Own GA4 reporting, custom dimensions and metrics design, tracking plan validation, and GA-to-warehouse integration.
Advise on tagging strategy for new product features and platform launches in partnership with the Lead MarTech Solutions Architect.
Design, build, and maintain high-performing Explores and dashboards in Looker for business stakeholders including Marketing, Product, Finance, Engineering, Account Management, and Support/Operations — owning deliverables end-to-end with minimal re-work.
Develop LookML for owned domains to a high quality standard — including complex dimensions, measures, derived tables, and Explore configurations.
Ensure all LookML is documented, tested, and consistent with organisation-wide semantic layer standards.
Ensure metric definitions in owned domains are canonical, unambiguous, and consistent with the definitions governed by the Analytics Engineering team.
Flag and resolve discrepancies between business intent and semantic layer implementation.
Expand self-service analytics capabilities — enabling non-technical users across Marketing, Customer Success, and Finance to answer their own questions independently, reducing ad-hoc request volume to the team.
Proactively maintain the accuracy and consistency of production Explores and dashboards as underlying data warehouse structures change.
Identify discrepancies before stakeholders encounter them.
Own the accuracy and freshness of all data products in scope.
Proactively identify and investigate data quality issues — with root-cause evidence — and partner with Analytics Engineers and Data Engineers to resolve upstream problems promptly.
Write high-quality, performant SQL — including complex window functions and CTEs — and collaborate with Data Engineers on query optimisation, compute cost efficiency, and derived table design for owned analytical workloads.
Collaborate with Analytics Engineers on LookML architecture and semantic layer design for owned domains — providing analytical requirements that inform model design and validating that data models correctly reflect business logic.
Partner with the Senior BI & AI Analyst on customer analytics definitions for segmentation, LTV, and churn use cases.
Consume ML model outputs from the Data Science team and surface them accurately in dashboards and analytical narratives.
Understand the structure of data pipelines feeding owned dashboards sufficiently to diagnose data freshness and quality issues, escalate to the correct team, and communicate resolution timelines to stakeholders.
Own coding standards and analytical best practices for the team — including SQL style guides, LookML conventions, documentation requirements, and PR review processes.
Conduct structured code reviews that are substantive, consistent, and designed to build capability in Junior Analysts.
Mentor Junior Data Analysts through paired analysis, direct feedback, and structured review.
Track improvement over time.
Escalate development needs or blockers to the Manager.
Build and contribute reusable assets to shared team libraries — including SQL query patterns, LookML components, analysis frameworks, and documentation templates — reducing time-to-delivery for the entire team, not just own work.
Contribute to at least one standards update, governance policy, or best-practice guide per year.
Apply LLM and AI tools (Gemini, GPT-4, Cortex Analyst, or equivalent) hands-on to accelerate analytics workflows — including automated insight narration, anomaly detection and explanation, and AI-assisted dashboard annotation.
Design and build self-serve analytics agents that enable non-technical stakeholders in Marketing, Customer Success, Finance, and Account Management to query data and surface insights in natural language — without raising tickets or requiring analyst intervention.
Own the full lifecycle from prompt design through output validation and iteration.
Configure Cortex Analyst or equivalent AI-assisted BI platforms to extend self-service beyond traditional dashboard interaction — including semantic model templates and YAML definitions that produce accurate, governed responses to unstructured business questions.
Define quality standards for AI-generated analytical outputs — including accuracy validation, hallucination detection, and a review process before AI-generated insights are shared externally or with executives.
Prototype and propose AI tooling that reduces repetitive analytical work for the team.
Present prototypes to the Manager with a clear build-vs-buy recommendation and documented lessons learned that contribute to the team's shared AI analytics playbook.

Benefits

medical, dental, vision and FSA benefits
Healthcare and Dependent Care reimbursement programs
Employee Assistance Program (“EAP”)
Optum Care 24-hour confidential medical counseling services
ten (10) paid holidays per calendar year
15 days of PTO in the first 12-months of continuous service, and 22 days in subsequent years
Eligible part-time employees earn pro-rated PTO
401(k) Plan
college tuition and education assistance programs
Udemy Learning courses
ongoing learning and development opportunities