Senior Data Engineer

Role Overview

• Build, maintain, and optimize data pipelines across a variety of source systems.
• Support and improve our core data warehouse infrastructure, primarily in Snowflake, with some legacy warehouse environments such as Redshift.
• Develop and maintain transformation logic, models, and reusable data assets using tools such as dbt.
• Build new warehouse functionality, curated data models, marts, and tables that support reporting, analytics, operations, and stakeholder decision-making.
• Support BI and reporting workflows across Looker and Domo, partnering with analysts and business teams to ensure trusted, consistent metrics.
• Manage and troubleshoot existing data pipelines, jobs, connectors, data shares, SFTP connections, APIs, and native integrations.
• Write and maintain production-quality SQL, Python scripts, and transformation workflows.
• Partner with analysts and business stakeholders to understand data needs and translate them into reliable, scalable data solutions.
• Help ensure our data is accurate, timely, well-documented, and trusted by the teams that rely on it.
• Explore and adopt AI-assisted engineering tools such as Claude Code, Cursor, and other agentic AI frameworks to improve development velocity, documentation, testing, data quality, and operational efficiency.
• Support warehouse migrations, platform consolidation, and modernization efforts as the company continues to scale.
• Collaborate with cross-functional teams across marketing, sales, operations, finance, product, technology, and mortgage operations.
• Contribute to data quality monitoring, observability, governance, and process improvements.

Requirements

• 3–5+ years of professional experience in data engineering, analytics engineering, business intelligence engineering, or a similar data-focused role.
• Strong SQL skills and experience working with large, complex datasets.
• Experience building and maintaining production data pipelines.
• Experience with cloud data warehouses such as Snowflake, Redshift, BigQuery, or similar platforms.
• Experience with dbt or similar data transformation frameworks.
• Experience with Python or another scripting language used for data processing, automation, or pipeline orchestration.
• Familiarity with data integration patterns, including APIs, SFTP transfers, file-based ingestion, third-party connectors, data shares, and native platform integrations.
• Comfort working with BI and analytics tools such as Looker, Domo, Tableau, Power BI, or similar platforms.
• Interest in using modern AI tools to improve data engineering workflows, including AI-assisted coding, documentation, testing, code review, and automation.
• Comfort working with messy, real-world business data and turning it into clean, trustworthy, usable data assets.
• Strong problem-solving skills and attention to detail.
• Ability to work with both technical and non-technical stakeholders.

Tech Stack

• Amazon Redshift
• BigQuery
• Cloud
• Python
• SQL
• Tableau

Benefits

• Health insurance
• Flexible work arrangements
• Professional development