Senior Data Engineer — Fintech Platform & Data Pipelines

Dave; United States; Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

Flexible hours

Home office stipend

Medical/Dental/Vision insurance

Parental leave

401(k) with matching

Financial wellness support

Flexible PTO and company holidays

Job summary

Dave is seeking a Lead Data Engineer in the United States to own the data platform across multiple domains. You will blend hands-on engineering with technical leadership to design reliable, scalable infrastructure as the business grows.

You’ll partner with engineering and business teams to turn evolving needs into durable solutions. Expect ambiguity, architecture trade-offs, and durable data systems that power product decisions.

Qualifications

• 5+ years of software or data engineering with ownership of production data systems.
• Strong Python and SQL skills with solid fundamentals (testing, code review, VCS, CI/CD).
• Experience with modern cloud data warehouses (Snowflake, BigQuery, or Redshift).
• Experience with orchestration/transformation frameworks (Airflow/Cloud Composer and dbt).
• Strong data modeling, incremental processing, data quality, observability, and production recovery.

Responsibilities

• Lead design, implementation, and operation of production data pipelines and core platform capabilities.
• Build and improve integrations across APIs, files, CDC and event sources, cloud services, and internal systems.
• Establish patterns for data modeling, orchestration, testing, and data quality.

Skills

Python

SQL

Mentorship

Architecture leadership

Data governance

Tools

Snowflake

Airflow

dbt

BigQuery

Redshift

Job description

Dave is seeking a Lead Data Engineer in the United States to own the data platform across multiple domains. You will blend hands-on engineering with technical leadership to design reliable, scalable infrastructure as the business grows.

You’ll partner with engineering and business teams to turn evolving needs into durable solutions. Expect ambiguity, architecture trade-offs, and durable data systems that power product decisions.