Key skills
SQLPythonGitETL/ELT processesCloud-based data platformsSnowflakedbtDimensional modelingData quality testingData contractsReverse ETLWorkflow orchestration toolsCI/CD practicesAIMachine LearningMLETLELTData EngineeringAnalyticsLookerBITableauPower BIAirflowAWSVersion ControlCI/CDLeadershipCommunication
About this role
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
Requirements:
- 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)