Staff Analytics Engineer
- Compensation
- Not disclosed
- Location
- US
- Work setup
- Remote
- Seniority
- Staff
- Employment
- Full time
- Experience
- 5+ years
- Category
- Data analytics
- Posted
Skills
Must have
- 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.
Nice to have
- Fluency in Python or another programming language for automation, data processing, and engineering workflows.
- Familiarity with metadata management and data governance platforms such as Atlan.
- Experience building business intelligence dashboards, reporting frameworks, and self-service analytics solutions.
- Previous experience in education technology or highly regulated data environments.
- Passion for mentorship, knowledge sharing, teaching, and fostering a strong engineering culture.
- Open-source contributions, personal technical projects, or a demonstrated interest in exploring emerging data technologies.
Full description
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Analytics Engineer based in United States.
As a Staff Analytics Engineer, you will play a pivotal role in designing and scaling the data infrastructure that powers analytics, reporting, and strategic decision-making across a large education technology environment. You will lead the development of robust ELT/ETL pipelines, high-quality analytical data models, and semantic layers that transform complex datasets into reliable business insights. Working with modern technologies such as dbt, SQL, Airflow, and Snowflake, you will help build scalable data systems supporting millions of students and educators. You will collaborate closely with data scientists, software engineers, analysts, and business stakeholders to improve data accessibility, quality, and performance. Beyond hands-on engineering, you will mentor colleagues, establish technical standards, and influence the long-term analytics engineering strategy. This is an opportunity to combine technical leadership, architectural ownership, and meaningful impact in an organization focused on improving educational outcomes through data.
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.