Data Engineer II

Dave is a financial technology company building accessible financial products for underserved Americans. The Data Engineer II will build, maintain, and improve production data pipelines and platform capabilities, owning projects from implementation through ongoing support. The role also involves improving reliability, scalability, data quality, and efficiency while collaborating with analytics, data science, finance, risk, marketing, product, and engineering teams.

Responsibilities:

• Build, maintain, and improve reliable data pipelines that ingest, transform, and deliver data across Dave’s data platform
• Own data engineering projects and pipelines through implementation, testing, deployment, monitoring, troubleshooting, and ongoing support
• Work with Snowflake, dbt, Airflow/Cloud Composer, APIs, files, and cloud services to support production workloads and optimize them for reliability, performance, scalability, and cost
• Support warehouse development through thoughtful schema design, data modeling, testing, documentation, data quality practices, and query optimization
• Improve ingestion, orchestration, validation, retries, backfills, monitoring, and alerting while helping reduce recurring operational work
• Use AI-assisted engineering tools thoughtfully to accelerate development, debugging, documentation, and analysis while maintaining strong standards for accuracy, security, review, and engineering judgment

Requirements:

• 2+ years of software or data engineering experience, including experience building or supporting production data systems
• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent practical experience
• Strong programming skills in Python, Java, or another general-purpose language, plus strong SQL skills
• Experience building or maintaining ETL/ELT pipelines and working with technologies such as dbt, Fivetran, or similar tools
• Experience with cloud data warehouses such as Snowflake, BigQuery, or Redshift and orchestration tools such as Airflow or Cloud Composer
• Understanding of data modeling, data quality, relational data, and query and performance optimization
• Strong software engineering fundamentals, including version control, testing, code reviews, and CI/CD
• Ability to troubleshoot production systems methodically and communicate effectively with technical and business partners
• As a virtual-first company, team members can live and work anywhere in the United States, except Hawaii
• Experience with financial or other highly regulated datasets
• Familiarity with GCP technologies such as Dataflow, Pub/Sub, and Cloud Storage
• Exposure to streaming or event-driven data pipelines, data observability, or incident-management tooling
• Familiarity with GraphDB/Neo4j, data governance, privacy, access controls, or PII handling
• Experience supporting AI or machine learning data workflows, including pipelines, integrations, or data preparation for AI-powered applications