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Form of workPaperless Post is seeking an analytics engineer to help scale our data models and reporting analytics platforms. The ideal candidate has a deep understanding of data analytics concepts and experience working with cross-functional stakeholders. As an analytics engineer, you are responsible for ensuring high-quality, accurate data modeling (we use dbt) working to help others on the data team transform raw data into clean, modeled data that is ready for analysis by end users. This role reports to the VP of Engineering.
Responsibilities:
- Design, build and optimize data transformations using SQL in our data modeling layer (dbt).
- Create a consistent reporting layer to enable complex data analysis in our BI tools, Looker & Hex.
- Develop a deep understanding of the KPIs that drive our business and identify ways of improving overall results across the company.
- Build a strong relationship with stakeholders to establish confidence on key metrics by understanding their crucial needs.
- Curate insights using reports and dashboards and provide training on proper usage.
- Create documentation for technical and nontechnical teams on reporting definitions.
- Build internal data quality measures, tests, and processes to ensure reporting accuracy.
- Participate in data team ideation sessions to define analytics projects.
- Collaborate with data engineering processes and projects.
- Participate in an Agile development cycle.
- 4+ years experience in a data/analytics engineering role.
- Advanced knowledge of SQL and Python.
- Experience with reporting analytics and BI tools, preferably DBT and Looker.
- Experience working with cloud data warehouses.
- Familiarity with dimensional modeling database concepts, fact and dim tables.
- Experience working with and cleaning complex unstructured data.
- Ability to present complex information in an easy-to-understand and digestible manner.
- Ability to work cross-functionally with team members who are highly technical as well as those with limited or no statistical training.
- Comfort with the ambiguity that sometimes comes with a fast-paced start-up atmosphere.
- Familiarity with clickstream data and Google Analytics a plus
- Undergraduate or graduate degree in Statistics, Math, Data Science, Operations Research or related field a plus.
About the team
The Paperless Post data team plays a crucial role in our product's success. We get to dive into advanced data work-from marketing attribution models, growth analytics tactics, and product feature AB testing, to modern data engineering infrastructure and integrations-that solves complex business problems and increases the data functional capabilities of everyone throughout the company. On the reporting side, we help ensure that all teams have the information they need to make sound business decisions. Above all, we're a team that is excited about what we do.
About the company
Company-wide, we enjoy an amazing ecosystem of an even gender split and a balance of engineers and designers. Because Paperless Post isn't supported by ad revenue, we can focus our efforts on creating and improving on the ideal version of our platform, product, content, and partnerships for our users.
We are proud that Paperless Post has helped over 175 million people globally connect in the real world since our inception. Paperless Post exists to help all people celebrate all the moments that matter to them. We believe that having a team reflective of the diverse world around us empowers us to create a product that serves everyone. Women, people of color, trans/genderqueer individuals, individuals with disabilities, and veterans are especially encouraged to apply.
The compensation range for this role is $115,000 - 150,000 USD.
At Paperless Post, compensation is based on a number of factors, including geographic location, job-related skills, years of experience, and internal team banding. All full-time offer packages come with a base salary, equity component, and options for fully paid medical, dental, and vision benefits.The range posted here is based on the NYC market and may vary based on candidate location.
