Senior Data Engineer

Paperless Post is seeking a Senior Data Engineer to grow and enhance our ETL pipelines and data platform. The ideal candidate will be proficient and resourceful across a wide range of data engineering skills - including cloud infrastructure, software development, data orchestration, data warehouse design, and CI/CD. In this role, you will collaborate externally with engineering and product teams to capture and transform required data, and internally with analytics engineering and data science teams to support the launch of a broad range of data projects.

Responsibilities:

• Improve infrastructure reliability and stability through regular updates and error detection
• Build production level python ETL code to ensure high data quality
• Enable data science by productionizing and deploying machine learning models
• Partner with analytics engineering to help build optimized DBT SQL data models
• Collaborate with engineering and product to define required data structures upstream
• Design custom integrations with third-party tools to empower business teams
• Implement standardized data engineering procedures within the team, including the adoption of best practices for unit testing and Python runtime environments.

Qualifications:

• 5 years of experience in data engineering
• Advanced proficiency in Python (data manipulation and integration tasks) and SQL
• Strong experience with data pipelines, data orchestration, and CI/CD, utilizing tools such as Docker, Airflow, and CircleCI
• Broad understanding of cloud tools across both AWS (S3, Lambda, CloudWatch, EMR, ECS, EC2) and GCP
• Expertise working with cloud data warehouses, particularly BigQuery or Snowflake
• Experience with infrastructure as code, such as Terraform
• Solid understanding of relational and NoSQL databases as well as clickstream data
• Comfortable utilizing AI tools to enhance the efficiency of data engineering tasks
• Excellent communication skills, both written and verbal
• Desire and willingness to tackle new challenges and learn new technologies

Nice to have:

• Knowledge of SQL data modeling best practices using DBT
• Familiarity with BI tools such as Looker and Hex
• Comfortable navigating the ambiguity often present in a fast-paced startup environment

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 $150,000 - 175,000 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.