Senior Software Engineer, Parafin

• 4+ years of experience in software engineering with a strong background in data infrastructure, pipelines, and distributed systems
• Advanced proficiency in Python and SQL
• Hands-on Spark development experience
• Expertise with modern cloud data stacks—AWS (S3, RDS), Databricks, and Airflow—and lakehouse architectures
• Hands‑on experience with foundational data‑infrastructure technologies such as Hadoop, Hive, Kafka (or similar streaming platforms), Delta Lake/Iceberg, and distributed query engines like Trino/Presto
• Familiarity with ingestion frameworks, developer‑experience tooling, and best practices for data versioning, lineage, partitioning, and clustering
• Strong problem-solving skills and a proactive attitude toward ownership and platform health
• Excellent communication and collaboration skills, especially in cross-functional settings

Desirable

• We’re looking for a seasoned software engineer to join Parafin’s Infrastructure team and lead the development of our next-generation Data Platform
• This role is critical to ensuring that our data infrastructure is reliable, scalable, and developer-friendly as we continue to power financial services for small businesses
• As a Senior Software Engineer, you’ll be responsible for designing, building, and maintaining the systems that ingest, transform, and serve data across the company
• You’ll partner closely with Data Science, Platform Engineering, and Product Engineering teams to support data-driven product development and decision-making
• Design and build robust, highly scalable data pipelines and lakehouse infrastructure with PySpark, Databricks, and Airflow on AWS
• Improve the data platform development experience for Engineering, Data Science, and Product by creating intuitive abstractions, self‑service tooling, and clear documentation
• Own and maintain core data pipelines and models that power internal dashboards, ML models, and customer-facing products
• Own the Data & ML platform infrastructure using Terraform, including end‑to‑end administration of Databricks workspaces: manage user access, monitor performance, optimize configurations (e.g. , clusters, lakehouse settings), and ensure high availability of data pipelines
• Lead projects to improve data quality, testing, observability, and cost efficiency across existing pipelines and backend systems (e.g. , migrating Databricks SQL pipelines to dbt, scaling data ingestion, improving data-lineage tracking, and enhancing monitoring)
• Work closely with backend engineers and data scientists to design performant data models and support new product development initiatives
• Share best practices and mentor other engineers working on data-centric systems