Data Solutions Engineer

About Horizon Commerce

Job Description Summary

The Data Solutions Engineeris responsible fordesigning building andmaintainingscalable data pipelines and analytics models that support Horizon Commerces retail media and commerce intelligence platforms. This role focuses on data engineering and analytics enablement partnering closely with analytics product and client-facing teams to ensure high-quality reliable data used for measurement forecasting and optimization.

Key Responsibilities

• Design implement and maintain scalable data pipelines supporting retail media and ecommerce analytics platforms
• Develop and optimize dbt models and SQL transformations to ensure accuracy consistency and performance across datasets
• Manage data ingestion from multiple external sources including APIs retail media platforms and third-party data providers
• Monitor and troubleshoot workflow orchestration systems (e.g. Airflow) ensuring reliable and timely data delivery
• Partner with analytics and product teams to translate business requirements into efficient data models and metrics definitions
• Manage cloud-based data infrastructure (e.g. AWS services Airflow Snowflake) including permissions environments and usage optimization
• Implement and maintain version control documentation and CI/CD practices for data workflows to support transparency and reproducibility
• Proactively identify and automate manual or repetitive data processes to improve reliability and reduce operational overhead
• Collaborate with client-facing analysts and engineers to publish verified datasets and enable reporting forecasting and client deliverables
• Ensure data quality integrity and compliance through consistent validation monitoring and security best practices

Qualifications

• 5-8 years of experience in data engineering analytics engineering or data platform roles

• Experienceidentifyingand automating manual data processes

• Exposure to infrastructure best practices such as CI/CD for data workflows
• Experience supporting data shares and client-facing data delivery workflows
• Experiencemaintainingor developing web scraping pipelines (e.g. Selenium)
• Experience working with retail media or ecommerce platform data (e.g. Amazon Ads Walmart Connect Target Roundel)
• Strong communicationskills and the ability to explain technical concepts to cross-functional stakeholders
• Ability tooperateindependently and take ownership of critical production systems
• Experience using Git for version control and collaborative development workflows
• Experience managing service accounts API credentials and secure key-based authentication (e.g. public/private key management)
• Experience troubleshooting API integrations authentication issues schema changes and pipeline failures
• Familiarity with AWS services supporting data platforms including S3 Lambda EC2 IAM roles CloudWatch and related infrastructure components
• Experience managing data ingestion processes such asSnowpipeor equivalent streaming/batch ingestion frameworks
• Advanced SQL skills and experience working with cloud data warehouses such as SnowflakeBigQuery or Redshift
• Strong experience withdbtfor data modeling testing and transformation best practices
• Experience working with ELT tools such asFivetranand managing third-party data connectors
• Hands-on experience with workflow orchestration tools such as Airflow including DAG management scheduling monitoring and troubleshooting
• Strongproficiencyin Python for buildingmaintaining and troubleshooting production data pipelines

Preferred Qualifications

• Bachelors degree or equivalent experience in Computer Science Data Science Engineering or a related quantitative field
• Experience working with retail media or ecommerce data

• Prior experience supporting client-facing analytics or consulting teams
• Experience with Git-based version control and collaborative development workflows
• Experience with analytics or business intelligence tools
• Familiarity with measurement concepts such as incrementality attribution and forecasting
• Familiarity with retail performance metrics such as ROAS incremental revenue and new-to-brand measurement

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