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

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