Senior Data Engineer

Attain Finance

16h•$110,000 - $130,000

About The Position

We are seeking a Senior Data Engineer to join our growing team and help shape the future of our modern data platform. In this role, you will be responsible for designing, building, and maintaining scalable data solutions using Snowflake, Airflow, and AWS. You’ll partner with business and technical stakeholders to deliver high-quality data products while ensuring reliability, quality, and governance. This is a role for someone who thrives on independence and ownership. You’ll have the freedom to design and execute solutions end-to-end, while also mentoring teammates, driving best practices, and introducing new capabilities — including leveraging AI to automate workflows, documentation, and data management.

Requirements

5–8+ years of experience in data engineering or a related field.
Strong hands-on experience with Snowflake, Airflow, and AWS.
Proficiency in Python and SQL for building and maintaining data pipelines.
Experience with modern data modeling, ETL/ELT processes, and data governance best practices.
Ability to work independently and proactively, owning solutions from start to finish.
Experience or interest in applying AI/LLM-based tools to improve efficiency and automation.
Familiarity with CI/CD, GitHub workflows, and modern DevOps practices.
Excellent communication skills and the ability to collaborate with technical and non-technical stakeholders.
A track record of mentoring peers and driving best practices within a team.

Nice To Haves

Experience with OpenLineage, dbt, or schemachange for lineage and schema management.
Exposure to data observability tools (e.g., Monte Carlo, Soda, Great Expectations).
Familiarity with AI/LLM integration into data workflows for documentation, query generation, or anomaly detection.
Background in financial services, lending, or regulated industries a plus.

Responsibilities

Design and develop curated data products and pipelines using Snowflake, Airflow, Python, and AWS.
Own the full lifecycle of data workflows — from requirements to deployment — with minimal supervision.
Implement and improve frameworks for data quality, lineage, and observability.
Use AI-driven tools to automate repetitive tasks such as documentation, data lineage extraction, and anomaly detection.
Maintain and enhance documentation of data pipelines and products, ensuring transparency and ease of use across teams.
Partner with analysts, data scientists, and product teams to deliver data assets optimized for analytics and machine learning.
Support production data operations, troubleshoot issues, and continuously optimize performance and cost.
Mentor team members, share knowledge, and contribute to the overall direction of the data engineering practice.