Leader of Analytics Engineering

United Stateslead

AnalyticsData & AI

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Quick Summary

Key Responsibilities

Lead, inspire, and deeply mentor a team of analytics engineers and data modelers, cultivating software engineering best practices, robust testing frameworks, version control,

Requirements Summary

6+ years of progressive experience in analytics engineering, data engineering, or enterprise data warehousing,

Technical Tools

AnalyticsData & AI

Thanks for your interest in Success Academy! Running a large, fast-growing, and high-performing network of public charter schools takes a village - families, children, teachers, staff and faculty, advocates, and supporters alike. We are growing fast in New York and expanding to Florida, and we would love to welcome you to our community! We work tirelessly every day to ensure children have access to a fun, rigorous, whole-child education regardless of zip code or economic status. When you join SA, you play a part in giving every student who walks through our doors a fair shot at reaching his or her potential.

We are seeking a visionary, technically grounded Leader of Analytics Engineering & Semantic Architecture to guide our data modeling and technical analytics team. In this role, you will lead the evolution of Success Academy’s enterprise data warehouse presentation layer. You will own the strategy, design, and optimization of unified semantic layers that power next-generation BI platforms and emergent AI-driven insights. As a technical leader, you will manage a team focused on transforing raw warehouse models into highly performant, well-governed, and business-ready presentation assets.

Responsibilities

~1 min read

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Team Leadership & Analytics Engineering: Lead, inspire, and deeply mentor a team of analytics engineers and data modelers, cultivating software engineering best practices, robust testing frameworks, version control, and technical execution excellence.
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Semantic Layer Architecture: Design, build, and optimize enterprise-grade semantic (presentation) layers on top of our modern cloud data warehouse to establish a unified single source of truth across operational, financial, and academic domains.
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Next-Gen AI & Analytics Support: Structurally architect data presentation models to seamlessly support modern analytics frameworks, natural language querying tools, automated anomaly detection, and emergent AI-driven insights engines.
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Data Modeling Standards & Pipeline Governance: Enforce modern analytics engineering standards— including dimensional modeling (Kimball star schema), dbt pipelines, DAG orchestration workflows, and automated data validation.
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Strategic Technical Alignment: Partner with data architecture, software engineering, and business leadership to translate complex institutional logic into scalable, self-serve data models, moving from ad-hoc ticket handling to proactive semantic infrastructure.

Requirements

~2 min read

Experience: 6+ years of progressive experience in analytics engineering, data engineering, or enterprise data warehousing, with at least 2–3+ years successfully managing direct technical reports in a modern data environment.
Advanced Analytics Engineering & SQL: Expert-level SQL skills paired with hands-on mastery of dbt (data build tool), pipeline orchestration (e.g., Airflow, Dagster), and schema optimization over cloud data warehouses (e.g., Snowflake, BigQuery, Redshift).
Semantic Layer Expertise: Proven track record of architecting, improving, and maintaining robust presentation/semantic layers (e.g., dbt Semantic Layer, Cube, MetricFlow, LookML) tailored to support scalable analytics and AI/LLM readiness.
Data Architecture & Modeling: Deep, practical knowledge of dimensional modeling concepts (Kimball methodology), normalization, data lineage, and metadata management.
Technical Leadership & Quality Assurance: Demonstrated experience implementing CI/CD workflows, automated data quality testing frameworks, and managing technical delivery roadmaps.

Visualization & BI Platforms: Practical experience building reports or managing dashboards in traditional
BI tools (e.g., Tableau, Looker, Power BI).
Emergent AI Analytics Exposure: Direct experience evaluating or configuring semantic layers specifically for natural language querying engines, LLM context retrieval, or AI-assisted reporting tools.
Domain Context: Prior background or active interest working with education, non-profit, public sector, or enterprise operational datasets.

Exact compensation may vary based on skills and experience.

Compensation Range

$240,000—$260,000 USD

Success Academy Charter Schools is an equal opportunity employer and actively encourages applications from people of all backgrounds. Compensation is competitive and commensurate with experience. Success Academy offers a full benefits program and opportunities for professional growth.

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We are an equal opportunity employer and value diversity at our organization. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We actively seek applications from people of all backgrounds to strengthen our community and the perspectives needed to flourish in a multicultural world. Success Academy offers a full benefits program and opportunities for professional growth.

Success Academy Charter Schools does not offer employment-based immigration sponsorship.