[Remote] Data Engineer

Note: The job is a remote job and is open to candidates in USA. Sennos is rapidly emerging as the global leader in AI-driven sensing, analytics, and control for the Fluid, Fermentation, and Bio-manufacturing industries. The Data Engineer role focuses on building and maintaining data pipelines, implementing transformations, and contributing to a reliable Snowflake-based warehouse that powers analytics, reporting, machine learning, and product capabilities.

Responsibilities

• Build and maintain ETL/ELT pipelines using SQL and Python under the guidance of senior data engineering leadership
• Develop and maintain transformations using dbt or similar tools within a Snowflake-based warehouse
• Create and optimize datasets and views to support analytics, reporting, machine learning, and product feature development
• Manage ad hoc data requests with accuracy and efficiency while maintaining data integrity and consistency
• Implement and maintain data quality checks, validation rules, and testing processes to ensure reliability and trust in warehouse data
• Support the enforcement of data contracts between source systems and the warehouse
• Assist in reverse ETL workflows to operationalize warehouse data into downstream systems
• Contribute to ML data preparation and feature pipeline workflows
• Collaborate closely with Data Architecture, Analytics Engineering, Product, and Software Engineering teams
• Contribute to documentation, governance practices, and continuous improvement of data engineering standards

Skills

• Bachelor's degree in Computer Science, Data Science, Engineering, or related field (or equivalent years of professional experience)
• 2–4 years of experience in data engineering or a related data-focused role
• Experience working with ETL/ELT processes and structured warehouse data
• Exposure to cloud-based data platforms (AWS preferred)
• Strong SQL skills (joins, window functions, and query optimization fundamentals)
• Proficiency in Python for data processing, scripting, or automation
• Familiarity with version control systems (e.g., Git)
• Strong attention to detail and commitment to data accuracy
• Ability to troubleshoot and debug data workflows effectively
• Strong written and verbal communication skills
• Ability to collaborate across technical and non-technical teams
• Experience working with Snowflake or similar cloud data warehouses
• Exposure to dbt or similar transformation frameworks
• Introductory experience with dimensional modeling concepts
• Experience implementing data quality tests or validation frameworks
• Exposure to data contracts or schema management practices
• Familiarity with reverse ETL concepts
• Passing experience with workflow orchestration tools (e.g., Airflow, Dagster, or similar)
• Familiarity with CI/CD practices for data workflows
• Experience using AI-assisted tools to support debugging, pipeline development, or data engineering workflows
• Exposure to BI tools (e.g., Power BI, Tableau, Looker)

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