Senior Analytics Engineer

Roles and Responsibilities

• Data Modeling and Transformation: 
• Take ownership of the transformation layer in our data stack, developing, testing, and deploying robust, scalable, and well-documented data models using dbt and advanced SQL. 
• Apply data modeling best practices, including dimensional modeling (Kimball), to structure raw data—including heavily nested JSON from our application databases (MongoDB)—into performant and intuitive fact and dimension tables. 
• Collaborate with stakeholders (including Product, Engineering, Finance, Acquisition, and Business Development) to drive deeper, data-informed decision-making across the company. 
• Develop and maintain curated data layers that serve as the "single source of truth" for downstream consumption in our BI tool (Omni) and reverse ETL platform (Hightouch). 
 
• Applying Software Engineering Practices to Analytics: 
• Champion and implement software engineering best practices within the analytics workflow, including version control (Git) for all dbt models and CI/CD pipelines for automated testing and deployment. 
• Build a comprehensive suite of automated data quality and integrity tests (e.g., using dbt tests, custom Python scripts) to ensure the reliability and trustworthiness of all data products. 
• Create and maintain extensive documentation and a data catalog, ensuring that key business metrics are clearly defined and that valuable datasets are easily discoverable by all users. 
 
• End-to-End Analytics Development and Business Partnership: 
• Serve as the primary analytics partner for key business functions, embedding directly with stakeholders in Product, Finance, and Marketing to understand their strategic goals and challenges. You will independently translate their ambiguous questions into formal technical requirements and project documentation. 
• Execute on the requirements you've defined by personally designing, building, and implementing the curated, reliable data models in dbt that power our BI platform (Omni). Your role extends beyond just enabling self-service; you will often conduct the initial deep-dive analysis yourself to deliver immediate answers and insights back to the business. 
• Use Python and AWS Lambda where appropriate to enhance our analytics capabilities, such as for performing advanced data quality checks or automating data enrichment tasks. 
 
• Project Ownership: 
• Lead the development of critical data assets from end-to-end, including requirement gathering, technical design, implementation in dbt, and validation with business users. 
• Take ownership of specific business domains within our data warehouse (e.g., product engagement, customer lifecycle, financial performance), becoming the go-to expert for that data. 
• Proactively identify opportunities to improve data model performance, simplify logic, and increase the business impact of our analytics platform. 

What We’re Looking For

• Proven experience as an Analytics Engineer, or a similar role such as a BI Developer or data-focused Software Engineer, with a strong track record of building production-grade data models. 
• Core expertise in dbt, including developing and deploying complex models, writing macros, and implementing a robust testing and documentation framework. 
• Expert-level, production-grade SQL proficiency, with the ability to write complex, highly-performant queries and transform intricate business logic into code. 
• Strong hands-on experience with a cloud data warehouse, particularly Snowflake, with a focus on advanced usage, query optimization, and leveraging platform features for efficient modeling. 
• Deep, practical understanding of data modeling principles, especially Kimball's dimensional modeling (star/snowflake schemas). 
• Experience transforming and un-nesting complex, semi-structured data sources, particularly nested JSON. 
• Proficiency in Python for scripting data quality tests, automation, and light API interactions. Familiarity with deploying scripts on AWS Lambda is a plus. 
• Experience creating curated data layers to power BI tools (Omni or similar) and reverse ETL platforms (Hightouch). 
• A disciplined approach to applying software engineering best practices to analytics, including Git, CI/CD, and automated testing. 
• Excellent communication and collaboration skills, with a demonstrated ability to translate ambiguous business requirements into precise technical specifications for both technical and non-technical audiences. 
• Experience with data ingestion tools like Fivetran and event tracking platforms like Fullstory or Amplitude. 
• A proactive, product-oriented mindset with a passion for building trustworthy data solutions that have a measurable business impact. 

What You’ll Do in 30 Days

• Gain a deep understanding of our business logic and key metrics by diving into our dbt project and core Omni dashboards. 
• Familiarize yourself with our data architecture, including how data flows from sources like Fivetran and our application database through Snowflake and into Omni and Hightouch. 
• Meet with key stakeholders in Product, Finance, and Marketing to understand their primary analytical challenges and how they use data today. 
• Get your development environment fully configured and make your first contributions to our dbt project, such as improving documentation, adding tests to an existing model, or fixing a bug. 

What You’ll do in 60 Days

• Take ownership of your first data domain (e.g., Engineering and Product Management data from Jira). 
• Begin refactoring and optimizing a core set of dbt models to improve performance, clarity, and reliability, implementing a robust suite of data quality tests. 
• Deliver your first significant enhancement to a business-facing dataset, collaborating with an analyst to validate the changes and roll them out in Omni. 
• Establish yourself as a key resource for stakeholders in your owned domain, proactively answering questions and providing guidance on data usage. 

What You’ll do in 90 Days

• Independently lead the development of a major new data model from scratch, from gathering stakeholder requirements to designing the schema and deploying the final, tested models to production. 
• Demonstrate clear business impact by launching a new set of analytical capabilities powered by the data models you’ve built. 
• Become a primary advocate for data quality and best practices, mentoring other team members and confidently shaping the technical direction of our analytics code base. 
• Proactively identify and scope your next major project, demonstrating a deep understanding of both our technical stack and our strategic business priorities.