Numerator is looking for a Data Engineer II to help us drive decision-making, find bigger opportunities, and work with our established and rapidly evolving platforms. In this position, you will be responsible for taking on new initiatives to automate, enhance, maintain, and scale services in a rapidly scaling environment.

As a Data Engineer II at Numerator, you will help our team deliver data products, analytics, and models quickly and independently. The role is cross-functional and responsible for developing resilient data pipelines and infrastructure for evaluating and deploying data science models.

The ideal candidate should be experienced with processing large quantities of data, building algorithms alongside software engineers, data warehouse and/or service architecture, and using declarative infrastructure and Kubernetes.

You will have a broad impact and exposure across Numerator as you help build out and expand our technology platforms across several software products. This is a fast-paced role with high growth, visibility, and impact, and where many of the decisions for new projects will be driven by you and your team from inception through production.

What you get to do:

• Collaborate with Product, Analytics, Data Science, and Engineering teams to build or enhance data products while ensuring adherence to data quality standards

• Lead complex, end-to-end projects focused on improving data quality and ensuring statistical models (e.g., sampling, segmentation, classification, predictive modeling) are supported by clean and validated data

• Design and develop pipelines that enforce data validation, quality checks, and best practices for integrating Data Science models into customer-facing products

Requirements:

• 2+ years of experience in data engineering, data warehousing, or related roles with a strong focus on data quality.

• Proficiency in Python (preferred) or another major programming language, along with SQL, with experience in implementing data validation and transformation processes.

• Expertise in data modeling, ETL/ELT design, and workflow orchestration (preferably using Airflow), ensuring data transformations meet business needs while maintaining integrity and quality.

• Experience with GitHub, including version control best practices, branching strategies, and collaboration workflows.

• Familiarity with Machine Learning or Statistical Model Development processes and their dependence on high-quality data.

• Experience designing and deploying cloud-based production solutions (AWS, Azure, or GCP), with a focus on maintaining data quality across environments.

• Strong attention to detail, intellectual curiosity, and a commitment to delivering high-quality data in a fast-paced, collaborative environment.

Nice to Haves:

• Experience with AWS or any cloud-based certifications (e.g., AWS Certified Solutions Architect – Associate, AWS Certified Developer – Associate).

• Experience with Terraform and/or Ansible (or similar) for infrastructure deployment.

• Experience with Airflow, including building and monitoring DAGs and developing custom operators.

• Experience with Snowflake, Databricks, or another data warehouse.

• Experience working with containerized services such as Docker and Kubernetes.

• Familiarity with Tableau or any data visualization tool

• Experience working with marketing insights, shopping data, or in the retail industry.

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