Analytics Engineering Manager (26-27)

IDEA Public Schools•El Paso, TX

About The Position

The Analytics Engineering Manager leads the team responsible for transforming raw data into trusted, business-ready data products that drive student outcomes across IDEA's network. This leader ensures that clean, validated, well-documented data models power dashboards, reports, and analytics that help teachers improve instruction, principals run campuses effectively, and executives make strategic decisions. Reporting to the Managing Director of Data Platform & Engineering, this role translates business requirements into scalable data transformations using dbt, establishes data quality standards, and builds domain-oriented data products that serve Analytics, Business Intelligence, Data Science, and Research teams. This leader manages the analytics engineering team through IDEA's transformation from legacy reporting to modern, self-service analytics enabled by the Snowflake data lakehouse. This role is central to achieving IDEA's goal of enabling trusted, consistent data definitions across all states and departments—ensuring that "enrollment" means the same thing in Texas, Ohio, and Florida, and that stakeholders can access reliable data without waiting weeks for custom reports.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Science, Statistics, Mathematics, or a related field, or equivalent practical experience.
7+ years of experience in analytics engineering, data engineering, data analytics, or closely related technical roles.
3+ years of experience in technical leadership or people management, leading analytics, data, or BI teams.
Demonstrated hands-on experience with dbt (2+ years) building and maintaining production data models and transformations.
Strong data modeling expertise, with a proven track record designing dimensional models, analytics data marts, or business-facing data products.
Expert-level SQL skills, including complex analytical queries and performance optimization
Experience partnering with non-technical stakeholders to gather requirements and translate them into effective technical solutions.
Deep hands-on expertise with dbt (Cloud or Core), including model development, testing, macros, packages, documentation, scheduling, and performance optimization.
Strong command of dbt project structure, materializations (including incremental models and snapshots), and integration with BI-owned metric certification and semantic layers.
Ability to evaluate when to leverage community dbt packages versus building custom solutions.
Expert-level SQL for complex analytical transformations and performance optimization.
Strong data modeling skills across dimensional (Kimball), Data Vault, and domain-oriented patterns, including temporal modeling, SCDs, and surrogate keys.
Proven judgment in balancing normalization vs. denormalization for performance, flexibility, and downstream analytics use cases.
Experience designing and implementing automated data quality testing and validation frameworks.
Familiarity with data quality tooling (e.g., Great Expectations) and core data quality dimensions across analytics workflows.
Familiarity with modern analytics stacks and how analytics engineering integrates with cloud data platforms, ingestion tools, dbt, and BI systems.
Working knowledge of DataOps practices such as version control, CI/CD, and automated testing.
Knowledge of K–12 education data domains and metrics, including enrollment, attendance, assessments, staffing, and multi-state reporting requirements.
Familiarity with education data privacy (FERPA), academic calendars, and operational rhythms.
Proven ability to lead technical teams, facilitate requirements and design discussions, and manage competing stakeholder priorities.
Strong communication and change management skills, translating technical capabilities into clear business value.

Nice To Haves

Master’s degree in Data Science, Statistics, Computer Science, or a related analytical field.
dbt Analytics Engineering certification or equivalent demonstrated expertise
Hands-on experience with Snowflake or comparable cloud data warehouse platforms.
Experience working with K–12 education data, student information systems, or education analytics.
Experience building data solutions for multi-state or geographically distributed organizations.
Exposure to data governance practices, including business glossaries and data quality frameworks
Familiarity with modern data stack tools (e.g., ingestion, orchestration, BI, and data quality platforms).
Experience leading analytics teams using Agile or iterative delivery methodologies.

Responsibilities

Operate as a player-coach, balancing 40–50% hands-on technical contribution with people leadership, coaching, and delivery oversight.
Own the design and implementation of analytics engineering transformations using dbt, delivering scalable Bronze-to-Silver-to-Gold data models aligned to enterprise architecture.
Establish and enforce dbt standards, project structure, testing patterns, and documentation practices.
Personally deliver complex, high-impact data models while coaching team members on domain modeling and best practices.
Design domain-oriented, multi-state data models with consistent definitions, supporting BI semantic layers and downstream analytics.
Implement performant transformation patterns (incremental models, snapshots, SCDs) and partner with Data Platform Architecture to align modeling with platform standards.
Implement approved metric definitions in dbt, with final metric certification owned by Business Intelligence.
Define, enforce, and operationalize data quality standards across all transformed data products, ensuring accuracy, completeness, consistency, and timeliness.
Establish data quality SLAs for critical domains and oversee automated testing, monitoring, and alerting in partnership with Platform Engineering.
Personally implement and review critical data quality tests for high-priority datasets.
Lead root cause analysis for data quality incidents, coordinating with Data Platform Engineering and Governance to resolve upstream issues.
Build a proactive data quality culture where issues are identified and resolved before stakeholder impact.
Serve as the primary interface between analytics engineering and business stakeholders across states and functional teams.
Translate business questions into clear data requirements and dbt model specifications that support operational reporting, BI, and advanced analytics.
Manage stakeholder expectations through transparent communication on timelines, priorities, and technical constraints.
Build trust by delivering reliable data products, clear documentation, and guidance on data usage, lineage, and metric interpretation.
Partner closely with Business Intelligence and Data Science to ensure analytics engineering outputs support dashboards, semantic layers, and advanced use cases.
Establish and enforce documentation standards covering business logic, sources, transformations, lineage, quality metrics, and known limitations.
Maintain data dictionaries, glossaries, and catalog entries in partnership with Data Governance.
Ensure onboarding materials and institutional knowledge are documented to prevent single points of failure and support team continuity.
Partner with Data Platform Engineering to ensure ingestion and Bronze layer design support downstream transformation needs.
Collaborate with DataOps on CI/CD pipelines, automated testing, and deployment standards for analytics engineering.
Align with Data Governance, Business Intelligence, and Research & Evaluation to ensure consistent definitions, stewardship, and methodological rigor.