Senior Software Engineer, Data Platform

About the position

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.

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).
• 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.

Requirements

• 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.

Nice-to-haves

• 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

• Salary Range: $205k - $250k
• Competitive equity grant
• Medical, dental, and vision insurance
• Unlimited PTO & flexible WFH policy
• Paid parental leave
• Free lunches & commuter benefits
• 401(k) and EAP support