Staff Analytics Engineer

Accountabilities: • Design, develop, and maintain scalable ELT/ETL pipelines using dbt, Airflow, SQL, and modern cloud-based data technologies.

• Build clean, reusable, and well-documented analytical data models and semantic layers that support business intelligence, reporting, and product analytics. • Architect and optimize data warehouse schemas, transformation workflows, and metrics systems to ensure performance, scalability, and long-term maintainability. • Develop reliable datasets that support student performance analysis, adaptive learning platforms, educational insights, and operational decision-making. • Design and maintain slowly changing dimensional models that accurately account for school-year transitions, student movements, and evolving educational data structures. • Improve data deployment workflows, testing infrastructure, CI/CD pipelines, and overall data reliability through automation and engineering best practices. • Establish data quality standards, monitoring practices, and validation processes to ensure consistency and accuracy across analytical systems. • Collaborate with data scientists, software engineers, analysts, and business stakeholders to translate business requirements into scalable data solutions. • Mentor analytics engineers and contribute to technical standards, documentation, code reviews, and knowledge-sharing initiatives. • Help define the long-term analytics engineering strategy, influence architectural decisions, and drive continuous improvements across the data organization.

Requirements:

• 8+ years of professional experience in software engineering, data engineering, analytics engineering, or a closely related technical discipline. • At least 5 years of specialized experience in analytics engineering, data modeling, or modern ELT/ETL systems. • Advanced proficiency in SQL and extensive hands-on experience with dbt, including developing and maintaining production-grade data transformation pipelines. • Strong expertise in analytical data modeling, particularly Kimball dimensional modeling principles and modern warehouse architecture. • Proven experience designing scalable data workflows, transformation processes, and semantic data layers. • Solid understanding of cloud data warehouse technologies, particularly Snowflake, and cloud-based storage systems. • Practical experience with orchestration and data ingestion tools such as Airflow, Fivetran, and Matillion. • Familiarity with business intelligence and semantic modeling platforms such as Looker, Tableau, or Cube.dev. • Strong knowledge of Git, automation, CI/CD pipelines, testing frameworks, and modern software engineering workflows. • Excellent analytical and problem-solving abilities, with a strong focus on data quality, reliability, and performance optimization. • Demonstrated technical leadership experience, including mentoring engineers, leading technical initiatives, and influencing engineering standards. • Strong communication and collaboration skills, with the ability to work effectively across technical and non-technical teams.