Sr. Data Engineer

We’re reinventing the market research industry. Let’s reinvent it together.

• Lead cross-functional initiatives to design, enhance, and scale data products that power analytics and customer-facing solutions.
• Own and deliver complex, end-to-end projects focused on improving data quality, integrating advanced statistical and machine learning models, and driving measurable business outcomes.
• Architect and develop pipelines with robust validation, quality enforcement, and efficient workflows for model deployment.
• Partner with data scientists to streamline model integration, ensuring reliability and performance in production systems.
• Define and promote engineering best practices, design standards, and technical excellence across the team.
• Mentor and guide junior engineers, fostering technical growth and knowledge-sharing.
  

• 5+ years of experience in data engineering, including designing and maintaining large-scale data platforms, building advanced data pipelines, and implementing production solutions.
• Proficiency in Python and SQL, with expertise in developing efficient, high-quality data transformations.
• Strong background in data modeling, ETL/ELT design, and orchestration tools (especially Airflow), ensuring scalability and integrity.
• Proven track record deploying cloud-based solutions in AWS, Azure, or GCP, with emphasis on resilience and reliability.
• Familiarity with Machine Learning/Statistical Model Development processes and their data dependencies.
• Demonstrated leadership in solving complex problems, with intellectual curiosity, attention to detail, and a focus on business impact.
  

What You'll Bring to Numerator

• Deep experience with Amazon Web Services (EC2, RDS, ECS, S3, Lambda, etc.).
• Expertise in Terraform, Ansible, or similar IaC tools for infrastructure automation.
• Advanced proficiency in Airflow, including DAG design, monitoring, and custom operator development.
• Hands-on experience with modern data platforms such as Snowflake, Databricks, or Redshift.
• Comfort working with containerized services (Docker, Kubernetes) in production.
• Background in retail, consumer insights, or marketing data is a strong plus.
• Ability to influence technical strategy, drive architectural decisions, and mentor team members.