Senior Data Engineer

Point Predictive is hiring a Senior Data Engineer in San Diego, CA (onsite) to build and run scalable data platforms for fraud detection and risk decisioning.

Responsibilities

• Design, build, and operate scalable pipelines ingesting and processing large volumes of lender, application, and transaction data across multiple formats and delivery methods
• Develop batch and real-time data processes supporting fraud detection, risk decisioning, model features, customer integrations, and production outputs
• Create and maintain data transformation workflows using dbt, SQL, Python, plus Snowflake, PostgreSQL, and AWS
• Improve platform scalability, reliability, and performance through architecture changes, query optimization, and automation
• Build streaming and event-driven pipelines using Kafka, Kinesis, Spark Streaming or similar platforms
• Convert complex data-processing, fraud, and business logic into reliable, maintainable production software
• Implement automated testing, monitoring, alerting, reconciliation, and data-quality controls across critical workflows
• Maintain data models that are clean, governed, and well-documented for downstream products, analytics, and machine-learning systems
• Investigate production issues and customer-reported data problems, identify root causes, and implement durable fixes
• Partner with Software Engineering, Data Science, Product, and customer-facing teams on large-scope technical projects
• Contribute to data architecture decisions and engineering standards
• Use AI-assisted development tools (for example, Claude Code) to improve productivity, testing rigor, and engineering quality

Requirements

• 5+ years of data engineering or software engineering experience building and operating scalable production systems
• Hands-on experience with Python, SQL, dbt, PostgreSQL, Snowflake, and AWS
• Experience designing and supporting production ETL and ELT, including batch and streaming pipelines
• Experience with event-driven technologies such as Kafka, Kinesis, Spark Streaming, or similar tools
• Strong understanding of data modeling, distributed systems, testing, observability, and production debugging
• Demonstrated ability to lead large technical projects and collaborate effectively across Engineering, Data Science, Product, and business teams
• High ownership and accountability, with clear communication and attention to quality
• Comfort using AI-assisted and agentic development tools while maintaining strong engineering judgment and review standards
• Experience in financial services, lending, fraud detection, or risk decisioning is a plus

Technologies

• Python, SQL, dbt
• Snowflake, PostgreSQL
• AWS
• Kafka, Kinesis, Spark Streaming
• Claude Code

Benefits

• 401(k)
• Dental insurance
• Flexible spending account
• Health insurance
• Health savings account
• Life insurance
• Paid time off
• Vision insurance

What Success Looks Like

• Reliable, well-tested batch and real-time pipelines move large volumes of data with minimal production issues
• Clean, documented, governed, and dependable data models for downstream systems
• Monitoring and automated controls identify pipeline and data-quality issues before customers are impacted
• Infrastructure scales efficiently as data volumes, customers, and product use cases grow
• Manual processes are replaced with durable, automated solutions
• Production issues and customer requests are resolved quickly and lead to lasting improvements
• Large technical projects are delivered with clear ownership and strong cross-functional partnership
• AI-assisted development tools are used thoughtfully to increase speed, strengthen testing, and reduce errors

Education

• Bachelor's or Master's

Location and Work Mode

• San Diego, CA 92101 (Required)
• In person

Salary

• USD 140,000 - 160,000 per year

Application Questions

• This is an In Office Job - Can you confirm that you will be available to work in office 5 days a week
• Our Core Values are: Get it Done, Pitch In, and Be the Expert. Describe in detail how you have embodied those Values in the past and how you will continue to work by them at Point Predictive