Analytics Engineer II

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for an Analytics Engineer II based in the United States.

The Analytics Engineer II will play a key role in transforming raw data into reliable, well-structured datasets that power reporting, self-service analytics, and informed business decisions. You will design, develop, and maintain scalable data models and transformation pipelines using modern cloud data technologies, including dbt and Snowflake. Working closely with data engineers, analysts, data scientists, and business stakeholders, you will turn complex analytical requirements into reusable datasets and consistent business metrics. You will take ownership of well-defined projects from requirements gathering and development through testing, deployment, and ongoing maintenance. In this role, you will also strengthen data quality, documentation, governance, and query performance while contributing to shared analytics standards. This remote opportunity offers an environment focused on collaboration, continuous improvement, and the responsible use of AI-assisted development tools to enhance engineering productivity and solution quality.

Accountabilities

Data Modeling and Transformation: Design, build, and maintain scalable data models and transformation pipelines using dbt and Snowflake, following established architectural patterns and engineering standards. Develop modular, reusable models that support reliable reporting, business intelligence, and operational analytics.
SQL Development and Optimization: Write, maintain, and optimize complex SQL queries and transformations, including joins, aggregations, and window functions. Troubleshoot query performance issues and improve the efficiency, accuracy, and reliability of analytical datasets.
End-to-End Solution Delivery: Own assigned analytics engineering tasks from requirements gathering and solution design through development, validation, deployment, and ongoing support. Manage priorities effectively, communicate progress, and escalate complex technical challenges or ambiguous requirements when appropriate.
Stakeholder Collaboration: Partner with business stakeholders to understand reporting needs and translate analytical requirements into accurate datasets, meaningful metrics, and reusable data structures. Collaborate with data engineers to integrate new data sources, resolve upstream dependencies, and maintain dependable data pipelines.
Data Quality and Testing: Develop and maintain dbt tests and other validation processes to identify inconsistencies, prevent data quality issues, and ensure trustworthy analytical outputs. Investigate data discrepancies, troubleshoot transformation failures, and implement corrective actions.
Metrics and Semantic Layer Development: Contribute to standardized metric definitions, semantic layers, and shared data structures that promote consistency across business units, reporting processes, and analytics platforms.
Documentation and Data Governance: Maintain clear documentation for data models, definitions, transformations, and lineage. Apply data governance and documentation standards that support transparency, traceability, and self-service analytics.
Code Quality and Collaboration: Participate in code reviews, use Git and pull request workflows, and provide constructive feedback to peers. Follow established development practices and contribute to team knowledge-sharing and continuous improvement.
AI-Assisted Engineering: Use AI-assisted development tools to support SQL generation, troubleshooting, testing, and documentation. Review, validate, and verify AI-generated code and outputs before implementation to ensure correctness, reliability, and maintainability.
Communication and Risk Management: Communicate technical concepts, project progress, dependencies, and potential risks clearly to technical and non-technical stakeholders. Seek guidance from senior engineers when technical complexity or uncertainty requires additional expertise.
Requirements
Professional Experience: Three to five years of experience in analytics engineering, data modeling, business intelligence, or a related data-focused role, with demonstrated experience delivering reliable analytical solutions.
SQL Expertise: Strong SQL skills, including complex joins, window functions, aggregations, query troubleshooting, and performance optimization. The ability to transform raw data into accurate, efficient, and reusable datasets is essential.
dbt Proficiency: Hands-on experience with dbt, including developing modular models, writing automated tests, maintaining documentation, and using basic Jinja templating.
Cloud Data Warehousing: Experience with Snowflake or a comparable cloud data warehouse, including an understanding of data transformation workflows and analytical data structures.
Data Modeling Fundamentals: Working knowledge of dimensional modeling, dataset grain, table relationships, and the principles of designing consistent, scalable analytical models.
Version Control and Development Practices: Experience using Git, pull requests, and collaborative code review workflows. Familiarity with software development standards, testing practices, and maintaining clean, well-documented code is important.
Data Quality and Governance: Understanding of data quality assurance, automated testing, documentation, governance, and data lineage principles, with the ability to investigate issues and maintain confidence in analytical outputs.
Independent Execution and Problem-Solving: Ability to deliver well-defined assignments independently, clarify requirements, troubleshoot technical problems, and recognize when additional guidance is needed. Strong attention to detail, curiosity, and a proactive approach to resolving challenges are essential.
AI-Assisted Development Tools: Experience using AI-powered engineering tools such as Cursor, Claude Code, GitHub Copilot, or similar technologies, with the judgment to validate generated code and results before use.
Communication and Teamwork: Strong written and verbal communication skills, including the ability to explain technical concepts to both technical and non-technical audiences and collaborate effectively across multiple teams.
Preferred qualifications:
Experience developing semantic layers, shared metric definitions, or data contracts.
Familiarity with modern business intelligence and visualization tools such as Sigma, Omni, Tableau, Power BI, or Looker.
Experience with data ingestion and integration tools such as Fivetran or Matia.
Familiarity with continuous integration and continuous delivery (CI/CD) workflows and automated deployment of analytics code.
Experience using Python for scripting, data processing, or automation.
Familiarity with AWS services used for data integration and processing.
Benefits
Remote work flexibility: The opportunity to work remotely from within the United States.
Collaborative work environment: A supportive and inclusive team culture that values diverse perspectives, knowledge-sharing, and teamwork.
Professional development: Opportunities to strengthen analytics engineering skills and gain experience with modern cloud data technologies, data modeling practices, and AI-assisted development tools.
Technical ownership: The opportunity to take responsibility for analytics engineering solutions from initial requirements through deployment and ongoing support.
Cross-functional exposure: Collaboration with data engineers, analysts, data scientists, and business stakeholders to understand business needs and deliver impactful data solutions.
Innovation and continuous improvement: The opportunity to improve data quality, streamline workflows, and contribute to scalable analytics infrastructure and consistent business metrics.
Inclusive workplace: A commitment to equal employment opportunities, mutual respect, and a workplace where diverse backgrounds and perspectives are valued.
Accessibility support: Reasonable accommodations are available to qualified applicants who need assistance during the recruitment process.