Leader of Analytics Engineering

Success Academy Charter Schools•New York, NY

3d•$240,000 - $260,000

About The Position

Success Academy is a growing network of public charter schools in New York and Florida, dedicated to providing a rigorous, whole-child education to all students, regardless of their background. We are seeking a visionary and technically grounded Leader of Analytics Engineering & Semantic Architecture to guide our data modeling and technical analytics team. This role is crucial for evolving our enterprise data warehouse presentation layer, owning the strategy, design, and optimization of unified semantic layers that power next-generation BI platforms and AI-driven insights. The leader will manage a team focused on transforming raw warehouse models into performant, well-governed, and business-ready presentation assets.

Requirements

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.
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).
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.
Deep, practical knowledge of dimensional modeling concepts (Kimball methodology), normalization, data lineage, and metadata management.
Demonstrated experience implementing CI/CD workflows, automated data quality testing frameworks, and managing technical delivery roadmaps.

Nice To Haves

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

Responsibilities

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.
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.
Structurally architect data presentation models to seamlessly support modern analytics frameworks, natural language querying tools, automated anomaly detection, and emergent AI-driven insights engines.
Enforce modern analytics engineering standards—including dimensional modeling (Kimball star schema), dbt pipelines, DAG orchestration workflows, and automated data validation.
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.