Data Engineer

Snyk•Boston, MA

About The Position

As a Data Engineer on our team, you will be responsible for transforming complex raw data into refined "data offerings" that empower the entire company. You won’t just be building pipelines in a vacuum; you will work as a strategic partner alongside analysts, product owners, and business leaders to ensure our data is intuitive, consistent, and ready for the next generation of data consumption. We are a team that values speed and high-leverage work. You should be an engineer who is not only comfortable with but enthusiastic about leveraging AI-assisted development tools to accelerate your workflow. You will play a key role in defining our central logic layer—ensuring that how we measure and talk about the business is standardized, interoperable, and easily accessible across the organization.

Requirements

3+ years of experience in a data engineering, analytics engineering, or high-level data architecture role.
Expertise in SQL: A deep understanding of how to write clean, performant, and modular code to transform data at scale.
An Efficiency Mindset: A track record of using modern tools to automate repetitive tasks and a genuine curiosity about how AI can enhance the engineering lifecycle.
A "Product" Mindset: A track record of working closely with stakeholders to turn business needs into actionable data plans and models.
Systems Thinking: Experience building data models that prioritize consistency, reusability, and clear documentation.

Nice To Haves

Experience with Snowflake or dbt.
Familiarity with building or maintaining a centralized semantic/metrics layer.
Hands-on experience integrating AI-assisted development into your daily coding workflow.
Proficiency in a scripting language such as Python.

Responsibilities

Collaborate Across the Business: Partner with a diverse range of stakeholders to translate ambiguous business requirements into robust, scalable data architectures.
Build the Logic Layer: Design and manage the central semantic models that serve as the single source of truth for our core business metrics.
Accelerate with AI: Proactively use AI-assisted tools (such as GitHub Copilot or LLMs) to streamline code generation, automate documentation, and perform rigorous testing to move faster without sacrificing quality.
Drive Data Usability: Simplify the complexity of our data ecosystem by building data products that are easy for both humans and automated systems to navigate.
Ensure Integrity: Implement automated testing and observability frameworks to maintain high data accuracy and trust across the organization.
Enable Modern Use Cases: Optimize data structures to support emerging ways of interacting with data, focusing on interoperability and seamless access across different platforms.