Data Engineer II

Who We’re Looking For: Bachelor's degree in computer science, information systems, software engineering, or a related field — or equivalent practical experience. Three or more years of hands-on experience as a Data Engineer, Analytics Engineer, ETL/BI Developer, or similar role. Strong SQL skills, including window functions, CTEs, query tuning, and working with large datasets. Practical experience with a cloud data warehouse. Snowflake experience is strongly preferred; comparable experience with BigQuery, Redshift, Databricks, or Synapse will be considered. Working knowledge of data warehousing and dimensional modeling concepts (star and snowflake schemas, slowly changing dimensions, grain, surrogate keys). Proficiency in Python for data work, plus comfort at the command line. Experience working with semi-structured data such as JSON, including parsing and flattening it into relational structures. Experience with cloud platforms such as AWS or Azure (storage, IAM/access management, secrets, and basic compute). Experience with version control (Git) and collaborative development workflows. Understanding of what BI and reporting tools such as Power BI or Tableau need from an upstream data model. Strong analytical and problem-solving skills, with the ability to work both independently and as part of a team in a fast-moving environment. Clear written and verbal communication, including the ability to explain technical concepts to non-technical stakeholders. Preferred Qualifications:

Join Our Team as a Data Engineer! Do you enjoy being hands and want to help shape the data that our Marketing, BI, Finance, and Operations teams run on? This role works directly with our Director of Cloud & Data Engineering, who will set architectural direction and provide mentorship, while you own meaningful pieces of delivery end to end. We need someone who is proficient in Snowflake pipelines and transformations and is extremely analytical with strong problem-solving skills. If this sounds like you, we’d love to hear from you! Compensation: $100,000-$120,000 per year commensurate with experience What You’ll Do: Build, test, and maintain data ingestion and transformation pipelines in Snowflake using native tooling (Snowpipe, Streams and Tasks, Dynamic Tables, Snowpark, external stages, and SQL-based transformation frameworks). Migrate existing SnapLogic ETL jobs to Snowflake-native pipelines, validating parity, reconciling results against legacy outputs, and decommissioning legacy jobs cleanly. Contribute to the design and buildout of our new Snowflake data mart, including dimensional modeling, fact and dimension table development, incremental load patterns, and documentation. Develop and maintain integrations that land data from source systems — operational databases, SaaS platforms, APIs, and flat/semi-structured files — into Snowflake. Write performant SQL and tune queries, warehouses, clustering, and load patterns for both cost and speed. Implement data quality checks, testing, monitoring, and alerting so that pipeline failures and data anomalies are caught before downstream consumers see them. Support data consumers on the BI, Marketing, and Analytics teams by delivering clean, well-documented, well-modeled datasets, and by troubleshooting data questions as they arise. Follow and help improve our engineering practices: version control, code review, CI/CD for data pipelines, and environment promotion. Help uphold data security, privacy, governance, and access-control standards in partnership with the broader IT team. Maintain clear technical documentation, including data lineage, source-to-target mappings, and runbooks. Work through a prioritized backlog in collaboration with your manager and stakeholders, communicating status and blockers proactively. Who We’re Looking For:

Preferred Qualifications: Direct experience building Snowflake pipelines and transformations, or migrating from a legacy ETL tool (SnapLogic, Talend, Informatica, Matillion, SSIS, or similar) into a cloud-native pattern. Snowflake certification (SnowPro Core or Advanced) or a cloud platform certification (AWS, Azure). Familiarity with data governance and cataloging practices. Exposure to analytics and AI/ML platforms and tooling (e.g., Dataiku, Copilot, or LLM-based tooling in a data context). Local candidates or willingness to relocate preferred. Why You’ll Love Working Here:

Join Our Team as a Data Engineer! Do you enjoy being hands and want to help shape the data that our Marketing, BI, Finance, and Operations teams run on? This role works directly with our Director of Cloud & Data Engineering, who will set architectural direction and provide mentorship, while you own meaningful pieces of delivery end to end. We need someone who is proficient in Snowflake pipelines and transformations and is extremely analytical with strong problem-solving skills. If this sounds like you, we’d love to hear from you! Compensation: $100,000-$120,000 per year commensurate with experience What You’ll Do: Build, test, and maintain data ingestion and transformation pipelines in Snowflake using native tooling (Snowpipe, Streams and Tasks, Dynamic Tables, Snowpark, external stages, and SQL-based transformation frameworks). Migrate existing SnapLogic ETL jobs to Snowflake-native pipelines, validating parity, reconciling results against legacy outputs, and decommissioning legacy jobs cleanly. Contribute to the design and buildout of our new Snowflake data mart, including dimensional modeling, fact and dimension table development, incremental load patterns, and documentation. Develop and maintain integrations that land data from source systems — operational databases, SaaS platforms, APIs, and flat/semi-structured files — into Snowflake. Write performant SQL and tune queries, warehouses, clustering, and load patterns for both cost and speed. Implement data quality checks, testing, monitoring, and alerting so that pipeline failures and data anomalies are caught before downstream consumers see them. Support data consumers on the BI, Marketing, and Analytics teams by delivering clean, well-documented, well-modeled datasets, and by troubleshooting data questions as they arise. Follow and help improve our engineering practices: version control, code review, CI/CD for data pipelines, and environment promotion. Help uphold data security, privacy, governance, and access-control standards in partnership with the broader IT team. Maintain clear technical documentation, including data lineage, source-to-target mappings, and runbooks. Work through a prioritized backlog in collaboration with your manager and stakeholders, communicating status and blockers proactively.