3-5 years of experience in analytics engineering or related data roles.
Strong SQL skills, including complex joins and query optimization.
Hands-on experience with dbt for modeling and documentation.
Experience with Snowflake or similar cloud data warehouses.
Knowledge of dimensional modeling and dataset relationships.
Familiarity with Git and collaborative code review processes.
Understanding of data quality and governance principles.
Experience with AI-assisted development tools like GitHub Copilot.
Responsibilities
Design and maintain data models and transformation pipelines using dbt and Snowflake.
Develop and optimize SQL queries for reporting and analytics needs.
Own the entire process from requirements gathering to ongoing support.
Collaborate with stakeholders to translate analytical needs into structured datasets.
Partner with data engineers to integrate new data sources and troubleshoot dependencies.
Develop dbt models and documentation using reusable practices.
Investigate and resolve data quality issues and improve model performance.
Benefits
Opportunity to work with modern cloud data technologies and AI tools.
Collaborative team environment with cross-functional partnerships.
Focus on professional development through code reviews and knowledge sharing.
Engagement in innovative projects that impact business decision-making.
Full Job Description
Job Description
We are seeking a detail-oriented and results-driven Analytics Engineer II to join our Data Analytics Engineering team. You will build and maintain reliable, scalable, and well-documented data models that support reporting, self-service analytics, and business decision-making across the organization.
In this role, you'll collaborate with data engineers, analysts, data scientists, and business stakeholders to transform raw data into clean, reusable datasets and consistent metrics. You will independently deliver well-defined analytics solutions within established architectural patterns while partnering with senior engineers on more complex technical decisions.
Our team uses modern cloud data technologies and AI-assisted development tools to improve efficiency and quality across development, testing, troubleshooting, and documentation.
Responsibilities
Design, build, and maintain data models and transformation pipelines using dbt and Snowflake within established architectural patterns.
Develop and optimize SQL queries and transformations to support reporting, analytics, and operational needs.
Own assigned work from requirements gathering through development, validation, deployment, and ongoing support.
Collaborate with business stakeholders to clarify requirements and translate analytical needs into accurate, well-structured datasets and metrics.
Partner with data engineers to integrate new data sources and troubleshoot upstream dependencies.
Develop and maintain dbt models, macros, automated tests, and documentation using modular, reusable development practices.
Investigate and resolve data quality issues, troubleshoot transformation failures, and improve query and model performance.
Contribute to shared metric definitions and semantic layers to support consistent reporting across business units and analytics tools.
Maintain data definitions, documentation, and lineage to support governance and self-service analytics.
Participate in code reviews and knowledge-sharing, applying team standards and providing constructive feedback.
Use AI-assisted development tools to support SQL development, troubleshooting, testing, and documentation, validating generated code and results before use.
Communicate progress, dependencies, and risks clearly, seeking guidance when complexity or ambiguity requires additional support.
Qualifications
3-5 years of experience in analytics engineering, data modeling, business intelligence, or a related data role.
Strong SQL skills, including complex joins, window functions, aggregations, and query troubleshooting and optimization.
Hands-on experience with dbt, including modular models, testing, documentation, and basic Jinja.
Experience with Snowflake or a comparable cloud data warehouse.
Working knowledge of dimensional modeling, dataset grain, and table relationships.
Experience using Git, pull requests, and collaborative code review workflows.
Understanding of data quality, automated testing, documentation, governance, and data lineage principles.
Ability to independently deliver well-defined work, clarify ambiguous requirements, and troubleshoot data issues.
Experience using AI-assisted development tools such as Cursor, Claude Code, GitHub Copilot, or similar technologies.
Ability to communicate technical concepts effectively to both technical and non-technical audiences.
Attention to detail, curiosity, and a proactive approach to problem-solving.
Preferred Qualifications
Experience with semantic layers, shared metric definitions, or data contracts.
Experience with modern BI tools such as Sigma, Omni, Tableau, Power BI, or Looker.
Familiarity with data ingestion and integration tools such as Fivetran or Matia.
Familiarity with CI/CD workflows and automated deployment of analytics code.
Experience with Python for scripting or automation.
Familiarity with AWS services used for data integration and processing.
