Data Engineer ETL

Job Tite: Data Engineer

Location: Remote but interviews or laptop pickup must occur at one of the following locations: San Francisco, Arlington, VA, Denver, CO, Chicago, Boston, NYC, Houston, Miami, Los Angeles, Seattle, Dallas, Minneapolis, Minn, Birmingham, MI or Irvine, CA

Length: 6-10 months

ADF and Databricks is a must have. 

Data Engineer - ETL
We are seeking a Data Engineer to design, build, and scale high quality data solutions that power analytics, reporting, and data driven decision making. In this role, you will work across the full data engineering lifecycle—partnering closely with product, analytics, and engineering teams to deliver reliable, performant, and secure data platforms.
This role requires strong hands on technical expertise, ownership of complex data pipelines, and the ability to influence engineering standards and best practices.
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Key Responsibilities
• Design, develop, and maintain scalable batch and streaming data pipelines.
• Build and optimize ETL/ELT workflows to ingest and transform data from multiple sources.
• Develop and maintain data models that support analytics, reporting, and downstream applications.
• Ensure data quality, reliability, performance, and security across data platforms.
• Work with cloud based data platforms (Azure, AWS, or GCP) and modern data architectures (data lake, warehouse, lakehouse).
• Implement best practices for testing, monitoring, logging, CI/CD, and automation in data engineering.
• Collaborate with analytics, product, and business stakeholders to translate requirements into robust data solutions.
• Troubleshoot and resolve complex issues in non production and production environments.
• Mentor junior engineers and contribute to engineering standards, reusable frameworks, and technical documentation.
• Evaluate new tools and technologies to improve scalability, efficiency, and maintainability of data systems.
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Required Qualifications
• Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience).
• 5+ years of experience in data engineering, backend engineering, or platform engineering roles.
• Strong proficiency in SQL and experience with data modeling and performance optimization.
• Hands on experience with Python, Scala, or Java.
• Experience with distributed data processing frameworks (e.g., Spark or equivalent).
• Experience building and operating data solutions on cloud platforms (Azure, AWS, or GCP).
• Solid understanding of data warehousing, data lakes, and analytics ecosystems.
• Experience with version control, CI/CD pipelines, and DevOps practices.
• Strong problem solving skills and ability to independently own complex technical deliverables.
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Preferred Qualifications
• Experience with real time or streaming data platforms (e.g., Kafka or equivalent).
• Familiarity with workflow orchestration tools (e.g., Airflow, ADF, or similar).
• Exposure to data governance, security, and compliance concepts.
• Experience supporting analytics, BI, or machine learning use cases.
• Prior experience mentoring engineers or leading technical initiatives.