Note: The job is a remote job and is open to candidates in USA. Parafin is a company focused on empowering small businesses with financial tools. The Senior Software Engineer will lead the development of the Data Platform, ensuring its reliability and scalability while collaborating with various teams to support data-driven initiatives.
Responsibilities
- 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)
- Act as the primary engineering partner for the Data Science team—embedded closely to gather requirements, design scalable solutions, and provide end-to-end support on all engineering aspects of their work
- 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
Skills
- 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
- Experience with AWS infrastructure using Terraform
- Familiarity with observability tools (e.g., Datadog) and cost tracking in cloud environments
- Experience with financial systems or building platforms in a fintech setting
- Prior work on ML infrastructure: Feature stores (e.g., Tecton), ML model lifecycle (training, deployment, monitoring, retraining), real-time inference
- Contributions to internal tooling or open-source projects in the data ecosystem
Benefits
- Equity grant
- Medical, dental & vision insurance
- Work from home flexibility
- Unlimited PTO
- Commuter benefits
- Free lunches
- Paid parental leave
- 401(k)
- Employee assistance program
Company Overview
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