Senior Data Engineer

Possible Finance•Seattle, WA

13d•$165,000 - $185,000•Hybrid

About The Position

Since our founding, we have redefined how people approach small-dollar loans—delivering over $1 billion in funding to more than 1 million customers, issuing over 4 million loans, and saving our customers more than $500 million. At Possible, we’re building a new type of consumer finance company; one that helps our customers stay out of debt rather than profit from their staying in it. We are a Public Benefit Corporation with the mission to help communities unlock economic mobility through affordable credit products crafted to improve financial health for generations. Join the team that’s making our goal a reality. About Us We are seeking an experienced Lead Data Engineer to join our data team. Serving as an experienced Data Engineering professional, you will architect, build, and maintain sophisticated data models and pipelines. You will also mentor team members and lead the technical direction across the organization. Are you interested in leading data infrastructure initiatives while doing meaningful work? Have you always wanted your work to contribute positively to underserved communities? As a Senior Data Engineer at Possible, you will own and drive high-impact, large-scale data engineering projects from conception to production. Key responsibilities include: architect and deliver automated data pipelines at scale, build robust data ingestion and transformation frameworks from diverse internal and external sources to our cloud data lake, establish data security guidelines, and lead integration efforts with other systems at Possible. Our stack is mainly based on AWS, Databricks, dbt, Airflow, and Terraform. We are looking for a confirmed technical leader who can deliver sophisticated projects on time, mentor engineers, and leverage data to solve critical business problems. Possible is at an inflection point, transitioning into a multi-product, high-growth company. This senior role requires outstanding teamwork skills and engineering excellence. You will collaborate across teams to answer key strategic questions. You will guide architecture decisions and set data engineering standards that support Possible’s growth.

Requirements

demonstrated ability in data engineering or a similar role with demonstrated progression in technical complexity
Expert-level knowledge of SQL, Python, and Spark with validated ability to optimize performance at scale
Deep understanding of data modeling, architecture principles, and design patterns
Strong proficiency in cloud platforms (AWS, GCP, or Azure) and IaC tools like Terraform or AWS CloudFormation
Extensive experience with modern data warehousing solutions and data lake architectures
Advanced knowledge of orchestration tools (Airflow, Dagster, Prefect, AWS Step Functions)
Confirmed experience with dbt or similar transformation tools at enterprise scale
Strong background in implementing and carrying out data governance, security, and compliance practices
Experience mentoring engineers and leading technical initiatives
Excellent social skills with the ability to translate technical concepts to non-technical collaborators

Nice To Haves

Knowledge of machine learning operations (MLOps) and ML platform development
Experience with monitoring and observability tools such as DataDog, Grafana, Phenomenal Expectations, and Elementary
Track record of driving technical strategy and architecture decisions
Experience with data mesh or similar distributed data architectures

Responsibilities

Architect and implement highly scalable, reliable data pipelines and ETL processes
Build and optimize advanced data frameworks for analytics, reporting, and machine learning applications
Lead technical initiatives and mentor junior data engineers on guidelines
Partner with collaborators across finance, marketing, product, and engineering to define and support their data needs
Build and maintain cloud-based data infrastructure (AWS + Databricks) with a focus on scalability and cost optimization
Establish and implement data quality standards, monitoring systems, and SLAs
Evaluate, recommend, and implement new data technologies and architectural patterns
Drive data strategy and roadmap aligned with business objectives
Establish data engineering guidelines and documentation standards