Senior Data Engineer

Lower

Data Engineer II at Lower.com, focusing on building and maintaining data pipelines and warehouses. Collaborating with cross-functional teams to support decision-making across the company.

Posted 6/10/2026full-timeColumbus • Ohio, Texas • 🇺🇸 United StatesSeniorWebsite

Tech Stack

Tools & technologies

Amazon RedshiftBigQueryCloudPythonSQLTableau

About the role

Key responsibilities & impact

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

What you’ll need

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.

Benefits

Comp & perks

Health insurance
Flexible work arrangements
Professional development

ATS Keywords

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Hard Skills & Tools

SQLPythondbtdata pipelinesdata transformationdata warehousingdata modelingdata quality monitoringdata governancedata integration

Soft Skills

problem-solvingattention to detailcollaborationcommunicationstakeholder managementadaptabilityanalytical thinkingorganizational skillstrustworthinesscreativity