Why This Job is Featured on The SaaS Jobs
This Senior Data Engineer role is notable in the SaaS landscape because it sits close to the product surface, where data platforms directly influence how features are measured, improved, and governed. The remit spans core warehouse work in Snowflake alongside tooling for product and data science stakeholders, reflecting how modern SaaS companies treat analytics infrastructure as a product capability rather than a back-office function. The explicit focus on pipeline reliability, access control, and consistent reporting signals a mature data environment with real operational expectations.
For a long-term SaaS career, the combination of platform engineering and cost ownership is particularly transferable. Building ELT pipelines, enforcing data quality, and tuning performance are table stakes across subscription businesses, but pairing that with spend awareness is what differentiates senior practitioners as data estates scale. Collaboration with product and data science also builds the cross-functional judgment needed to translate ambiguous questions into durable datasets and metrics.
This role will suit engineers who prefer end-to-end accountability, from modeling to monitoring, and who enjoy making pragmatic tradeoffs between speed, correctness, and cost. It fits someone comfortable working independently while partnering closely with adjacent teams to standardize how data is produced and consumed across a SaaS organization.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.
We are looking for a skilled Snowflake Data Engineer to join our Product and Data Science team. The ideal candidate brings a solid background in data engineering, with deep expertise in Snowflake and cloud technologies.
In this role, you will design, build, and maintain robust data pipelines, ensuring our data infrastructure is scalable, reliable, and efficient. You will partner closely with product and data scientists, providing tools, automation, and best practices to accelerate their work, improve data quality, and ensure consistency across pipelines and dashboards.
Additionally, you will play a critical role in cost optimization for data operations, owning efficient data modeling and pipeline performance. This position is also key in building and scaling our enterprise data warehouse, helping to shape the future of our data ecosystem.
Responsibilities
Design, develop, and maintain scalable data pipelines using Snowflake and cloud technologies.
Own and manage cost optimization for Product and Data Science team operations.
Optimize pipelines for performance, reliability, and efficiency.
Build and maintain dashboards and reporting platforms using Streamlit.
Collaborate with cross-functional teams to gather requirements and deliver robust data solutions.
Work with modern AI tools, including Cursor, to accelerate development.
Implement data governance, security best practices, and access control policies.
Develop and enforce data quality checks, SLA monitoring, and dependency tracking.
Utilize version control systems (e.g., Git) and manage deployment workflows.
Proactively troubleshoot and resolve data-related issues.
Qualifications
Bachelor’s degree in Computer Science, Engineering, or a related field.
Proven hands-on experience with Snowflake, including data modeling, ETL/ELT development, and performance tuning.
Advanced proficiency in Python, with experience scripting and automating data workflows.
Proven experience working with Spark for large-scale data processing.
Demonstrated experience building Streamlit applications integrated with Snowflake.
Strong command of SQL for complex data querying and analysis.
Familiarity with cloud platforms such as AWS, Azure, or GCP, and services like S3, Redshift, or BigQuery.
Strong problem-solving and communication skills.
Ability to thrive both independently and as part of a collaborative, fast-paced team environment.
Preferred Qualifications:
Master’s degree in Computer Science, Engineering, or a related discipline.
Strong understanding of data warehousing principles, architecture, and best practices.
Experience working within Agile development frameworks (e.g., Scrum, Kanban).
Every Snowflake employee is expected to follow the company’s confidentiality and security standards for handling sensitive data. Snowflake employees must abide by the company’s data security plan as an essential part of their duties. It is every employee's duty to keep customer information secure and confidential.
Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.
How do you want to make your impact?
For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com
