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.

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.

Benefits:

• Competitive annual base salary ranging from USD 175,000 to USD 185,000 , depending on experience and qualifications.

• Eligibility for an annual discretionary performance-based bonus.

• Comprehensive medical, dental, and vision insurance coverage.

• 401(k) retirement savings plan.

• Mental health and wellness support.

• Paid time off and parental leave.

• Professional development opportunities and learning programs.

• Fully remote working environment.

• Opportunity to influence data architecture and analytics engineering strategy at an organizational level.

• Exposure to modern data technologies, large-scale analytical systems, and complex data infrastructure.

• Opportunity to mentor talented engineers and contribute to meaningful educational initiatives impacting millions of students and educators.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether?

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