Design and build scalable, production-grade data pipelines and data models to power downstream analytics and enterprise reporting
Lead the migration from a Redshift/DBT warehouse architecture to a modern lakehouse architecture (e.g., S3, Glue, Databricks, Unity Catalog)
Implement and enforce data validation standards, QA standards, and effective data lifecycle management
Optimize pipeline performance, cost, and data quality in a large-scale cloud data environment
Ensure compliance with data security and privacy regulations (e.g., GDPR, CCPA, GLBA) through robust pipeline design, access controls, and monitoring
Collaborate cross-functionally with App Engineering, BI, and business teams to translate ambiguous requirements into scalable, well-modeled datasets
Mentor data engineers and promote best practices in software engineering, documentation, and distributed data processing
Leverage AI-assisted development tools where appropriate to improve engineering efficiency, code quality, and observability

Requirements

4+ years of hands-on data engineering experience building and maintaining production ETL/ELT pipelines
Experience designing data architectures in cloud environments (AWS strongly preferred; Azure or GCP acceptable)
Expertise in distributed data processing frameworks (e.g., Apache Spark, Kafka, Hadoop, or similar)
Proficiency in Python (Pandas, NumPy, etc.) and advanced SQL for large-scale data transformation and querying
Experience with lake and lakehouse patterns, including open table formats (e.g., Iceberg, Hudi, Delta)
Experience tuning ETL performance and optimizing infrastructure costs in large-scale environments handling terabytes of data
Proven ability to model raw data into well-structured, analytics-ready datasets
Clear written and verbal communication skills, with the ability to explain complex technical concepts to non-technical stakeholders

Benefits

Competitive salary and company equity through Restricted Stock Units (RSUs), granted as part of our standard compensation package and based on role and level
401(k) with company match up to 4% of eligible earnings
Multiple medical plan options, plus dental and vision coverage
Company-funded HSA contributions (based on medical plan selection)
Company-paid life insurance and short-term disability
A variety of supplemental benefit options, including long-term disability, critical illness, accident, legal, and pet insurance
Access to mental health support and confidential counseling resources
Flexible PTO for exempt employees (most employees take 15–20 days per year), plus 8 company-observed holidays
Paid parental leave, including up to 14 weeks at 100% pay for birthing parents and 8 weeks at 100% pay for non-birthing parents
Career mobility and internal growth opportunities across the organization
Professional development budgets for certifications, conferences, and learning available, subject to management approval

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data engineeringETLELTdata architecturedistributed data processingPythonSQLdata modelinglakehouse architecturedata validation

Soft Skills

communicationmentoringcollaborationproblem-solvingdocumentation