Data Engineer (Snowflake)

Location: Remote, United States

Company

Build and operate data capabilities that help internal teams access trusted, usable, governed information for analytics, applications, automation, and AI-enabled workflows.

What you will do

• Build, maintain, and improve batch and low-latency ingestion pipelines from enterprise systems, APIs, and approved sources.
• Develop SQL and Python solutions to collect, validate, transform, and publish data.
• Use Snowflake and dbt to implement transformations, reusable models, curated datasets, and data products across raw, common, and curated layers.
• Apply AI-assisted workflows across coding, testing, documentation, troubleshooting, review, and coordinated multi-agent delivery while maintaining human validation and accountability.
• Partner with analytics, application, AI, Integration Platform, governance, security, and business teams to deliver governed and documented data access patterns.
• Monitor pipeline health, data freshness, performance, and failures; optimize Snowflake workloads and participate in incident resolution.

Requirements

• Bachelor’s degree in computer science, information systems, engineering, mathematics, or a related field, or equivalent experience.
• 3 or more years of experience in data engineering, software engineering, analytics engineering, or a related technical role.
• Professional experience writing production-quality SQL and Python.
• Experience building or supporting data pipelines, transformations, and data models in a cloud data environment.
• Experience with Snowflake, dbt, or comparable cloud data warehouse and transformation technologies.
• Understanding of data modeling, ELT/ETL patterns, pipeline orchestration, APIs, source-system integration, data quality, metadata, lineage, access control, privacy, and secure enterprise data handling.
• Experience with source control, code review, automated testing, and CI/CD.
• Demonstrated active use of AI-assisted software development tools for code generation, test creation, documentation, debugging, or review, plus the ability to follow an AI SDLC and identify opportunities for cooperating agents.
• Ability to investigate data issues, communicate findings clearly, work through ambiguity, and collaborate with engineers, analysts, product owners, governance partners, security teams, and business stakeholders.

Nice to have

• Experience with Azure services, serverless functions, cloud storage, or other cloud-native data engineering capabilities.
• Experience with REST or GraphQL APIs and ingestion from Salesforce, Hatch, NetSuite, or similar enterprise applications.
• Familiarity with orchestration, event-driven processing, observability, data catalogs, lineage tooling, or data quality platforms.
• Experience with semantic models, MCP-based access, governed interfaces, master data, reference data, entity resolution, or shared business definitions across multiple systems.
• Experience operating data products with documented ownership, access expectations, quality measures, and support procedures.
• Experience using AI agents or agentic workflows for software delivery, data engineering, testing, documentation, or platform operations.
• Curiosity about emerging data platform technologies and a practical approach to adopting them.

Culture & Benefits

• Remote, primarily computer-based work within an enterprise technology environment.
• Work in a product-oriented Agile engineering team with backlog refinement, estimation, code review, technical documentation, and iterative delivery.
• Regular, punctual attendance and adherence to applicable productivity, safety, and workplace standards are expected.