Data Engineer
Sennos, Durham, NC, United States
Sennos is rapidly emerging as the global leader in AI-driven sensing, analytics, and control for the Fluid, Fermentation, and Bio-manufacturing industries. With a revolutionary integration of hardware, software, and real-time multi-parametric data, we have quietly built the world’s most advanced sensing system and largest AI-powered fermentation data warehouse. By owning the entire stack—from sensing hardware to data infrastructure—we have delivered a breakthrough solution that reveals complex biological and chemical interactions, predicts precision outcomes, and enables next-generation production control.
Sennos is a rapidly growing start-up looking for someone who can quickly adapt to a changing environment and who has the desire to grow with us!
The Data Engineer sits within the Data & Analytics organization and supports the development and ongoing improvement of Sennos' modern data platform. This 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.
Working closely with senior data engineering leadership, data architecture, analytics engineering, and product teams, this role combines hands‑on technical execution with growing exposure to data modeling, quality enforcement, and scalable platform development.
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 systemsContribute 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
Required Qualifications
Education:
Bachelor's degree in Computer Science, Data Science, Engineering, or related field (or equivalent years of professional experience)
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)
Skills:
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
Preferred Qualifications
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)
Team Working Style
Collaborative and supportive, with strong mentorship from senior data engineering leadership
Focused on building durable foundations while moving quickly to meet evolving needs
Values curiosity, precision, and continuous skill development
Physical Requirements and Work Environment
Ability to sit for extended periods while working at a computer
Office setting with remote/hybrid flexibility
Minimal travel required (occasional team meetings or company events)
This job description is intended to convey information essential to understanding the scope of the position and is not an exhaustive list of skills, efforts, duties, responsibilities, or working conditions associated with it. Responsibilities may change according to business needs.
Our company is currently active in 16 states (AZ, GA, ID, IL, FL, MA, ME, MI, MO, NC, NY, TN, TX, OR, VA, and WA), and we prefer candidates located in one of these states for remote positions.
Equal Opportunity Statement
Sennos is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.
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Sennos is a rapidly growing start-up looking for someone who can quickly adapt to a changing environment and who has the desire to grow with us!
The Data Engineer sits within the Data & Analytics organization and supports the development and ongoing improvement of Sennos' modern data platform. This 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.
Working closely with senior data engineering leadership, data architecture, analytics engineering, and product teams, this role combines hands‑on technical execution with growing exposure to data modeling, quality enforcement, and scalable platform development.
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 systemsContribute 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
Required Qualifications
Education:
Bachelor's degree in Computer Science, Data Science, Engineering, or related field (or equivalent years of professional experience)
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)
Skills:
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
Preferred Qualifications
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)
Team Working Style
Collaborative and supportive, with strong mentorship from senior data engineering leadership
Focused on building durable foundations while moving quickly to meet evolving needs
Values curiosity, precision, and continuous skill development
Physical Requirements and Work Environment
Ability to sit for extended periods while working at a computer
Office setting with remote/hybrid flexibility
Minimal travel required (occasional team meetings or company events)
This job description is intended to convey information essential to understanding the scope of the position and is not an exhaustive list of skills, efforts, duties, responsibilities, or working conditions associated with it. Responsibilities may change according to business needs.
Our company is currently active in 16 states (AZ, GA, ID, IL, FL, MA, ME, MI, MO, NC, NY, TN, TX, OR, VA, and WA), and we prefer candidates located in one of these states for remote positions.
Equal Opportunity Statement
Sennos is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.
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