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Here at Lucid, data is key to making decisions that improve the product for our users, fuel the growth of the business, and allow the company to operate efficiently. As an Analytics Engineer, you will be responsible for producing and maintaining high quality data sets that enable other teams to quickly and accurately answer important questions that drive impact. You will accomplish this by cleaning, testing, documenting, and modeling data, all while ensuring that our data warehouse and transformation pipelines are reliable and performant. You will get to work with data from a variety of sources, including clickstream data, CRM systems, marketing platforms, subscription and payment data, and support tickets. Our data stack consists of Stitch, Fivetran, Airflow, Snowflake, Databricks, dbt, Hightouch, and Tableau, all of which you will use in this position.

This role is part of the Strategy and Analytics team, which supports the data and decision-making needs of every other function at Lucid. As such, you will have many opportunities to work cross-functionally with other teams. For example, you may work with data engineers on the ingestion of a new data source into our data warehouse or on moving data from the warehouse to other systems. You may also work with business leaders and stakeholders to help them self-serve to meet their own data needs or to automate a manual process. You may work closely with analysts on the Strategy and Analytics team to understand business needs and craft data sets to meet those needs. While analysts also contribute to data modeling, testing, and documentation, you will be an advisor and advocate in ensuring that we follow best practices and a technical expert when analysts run into difficult and complex data challenges.

To be a good fit for this role, you will need strong technical skills and an eagerness to learn new things. You should be highly organized and structured; things such as inconsistent naming conventions or coding style and messy model trees should stick out like a sore thumb. You should have an eye for things that can be automated or otherwise done in a better way. You should love figuring out the most efficient and performant way to write a SQL query or piece of code. You should be passionate about building systems and tools that create a solid, scalable foundation for other people’s work. You will play an instrumental role in building and maintaining that data foundation at Lucid, which will enable others to move forward with speed and confidence.

Responsibilities:

• Write complex, production-quality (i.e., accurate, performant, and maintainable) data transformation code to solve the needs of analysts, data scientists, and business stakeholders
• Implement effective data tests to ensure accuracy and reliability of data and ELT pipelines
• Assist in coaching and advising analysts on data modeling, SQL query structure and optimization, and software engineering best practices (e.g., version control, testing, code deployment)
• Assist in designing and maintaining the architecture and organizational structure of our data warehouse
• Collaborate with data engineers on infrastructure projects to implement new systems/tools/processes, ingest and model data from new sources, and pipe data between systems
• Troubleshoot and resolve data issues as they arise
• Ensure that data, systems, business logic, and metrics are well-documented
• Maintain the quality of our analytics codebase by cleaning up old code, identifying and addressing tech debt, and ensuring consistent style
• Other duties as assigned

Requirements:

• Bachelor's degree, ideally in a technical or quantitative field
• Comfortable using SQL for data transformations
• Familiarity with version control workflows
• Familiarity with Python or another modern programming language
• Ability to communicate clearly about data to both technical and non-technical audiences
• Experience partnering with people from different educational backgrounds and working collaboratively to solve problems
• Ability to manage time effectively and set and meet deadlines
• Passion for structure, organization, and efficiency, down to the details (e.g. maintaining consistent naming conventions and coding style)

Preferred Qualifications:

• Relevant experience working with data in an internship, former job, or through academic research
• Advanced degree, ideally in a technical or quantitative field
• Experience with dbt
• Experience with job scheduling platforms such as Airflow
• Experience modeling and working with data from third-party SaaS applications such as Salesforce, Marketo, Zendesk, Netsuite, etc.

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