Data Engineer

FocusKPI Inc.•San Francisco, CA

1d•$45 - $55•Remote

About The Position

FocusKPI is looking for a Data Engineer to join their client's growing team to help build and improve the data systems that power decision-making. The role involves developing reliable pipelines and platform capabilities while growing ownership of production data systems and the business outcomes they support. Data is central to how the client builds products, manages risk, understands members, and makes decisions. In this role, you will work alongside experienced Data Engineers and partners across Analytics, Data Science, Finance, Risk, Marketing, Product, and Engineering. You will own well-defined data engineering projects from implementation through production support and help make the platform more reliable, scalable, and efficient. There will also be opportunities to apply AI-assisted development tools and support emerging AI initiatives as they evolve how data is built and used.

Requirements

Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent practical experience.
4-5 years of industry experience in software or data engineering, including experience building or supporting production data systems.
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.
Hands-on experience building production pipelines for file ingestion into a data warehouse.
Experience with cloud data warehouses such as Snowflake, BigQuery, or Redshift and orchestration tools such as Airflow or Cloud Composer/ Good experience with Airflow.
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.

Nice To Haves

Experience in Financial Services or FinTech preferred

Responsibilities

Build, maintain, and improve reliable data pipelines that ingest, transform, and deliver data across the client'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.
Own end-to-end data lifecycle management — From ingestion and transformation to modeling, orchestration, and serving layers.
Ensure data quality, governance, and reliability — Implement testing frameworks, observability, monitoring, lineage, and data validation practices.
Drive platform optimization and cost efficiency — Continuously improve performance, scalability, and cloud cost management across the data ecosystem.
Establish engineering best practices — CI/CD, Infrastructure as Code, code reviews, documentation standards, and secure data handling.
Partner cross-functionally with technical and business stakeholders to translate data needs into reliable, production-grade solutions.