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# Data Engineer

- **Company:** Intermntain Elec Inc Price Uta
- **Location:** San Francisco, CA, United States
- **Salary:** $120,000.0 - $150,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Airflow, BigQuery, Cloud Database, Data Integrity, Extract Transform Load (ETL), Data Flow Control, Fraud Prevention and Detection, Python (Programming Language), Cloud Services, SQL Databases, Unstructured Data, Snowflake, Apache Spark, Google Cloud Functions, Real Time Data, Apache Kafka, Build Tools, Data Pipelines
- **Published:** September 5, 2026
- **Apply:** https://startup.jobs/data-engineer-baselayer-9891013

## About the Role

* 1+ years of experience in data engineering, working with Python, SQL, and cloud-native data platforms
 * Experience building and maintaining ETL/ELT pipelines in a production environment
 * Working knowledge of modern data stack tooling (e.g. Dataflow, Spark, Airflow or equivalents)
 * Hands-on experience with cloud data warehouses or lakes (e.g. BigQuery, Snowflake, or equivalents)
 * Solid data modeling fundamentals and real care for data integrity and reliability
 * Comfort with both structured and unstructured data, and a feel for what clean, scalable architecture looks like, * Curiosity about AI/ML infrastructure and a desire to be close to the models, not just the cleanup after them
 * Experience with streaming or real-time data systems (e.g. Kafka, Pub/Sub)
 * Exposure to KYC/KYB, fraud, risk, or underwriting data, and the ethical care that sensitive information demands
 * GCP experience (BigQuery, Cloud Run, Dataflow, Pub/Sub)
 * You care deeply about data quality and trust, and build systems others can rely on
 * You've worked without a playbook before, and you take direct feedback well and act on it fast

## Description

* Build and maintain ETL/ELT pipelines that ingest and normalize public records, web signals, and fraud telemetry from dozens of sources
 * Develop data models and transformation layers (Dataflow, Spark, Airflow) that power fraud detection, KYB, and customer-facing APIs
 * Implement data quality checks, observability tooling, and alerting so problems surface before customers see them
 * Tune pipelines and queries for performance, freshness, and cost in our cloud data warehouse
 * Work with data scientists, ML engineers, and product to make clean, well-modeled data available for entity resolution and scoring
 * Help ensure pipelines meet security and regulatory standards for sensitive data (SOC 2, GDPR, KYC/KYB)
 * Document what you build and translate between technical and non-technical stakeholders so the rest of the team moves faster


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