Senior Data Engineer

We’re looking for a Senior Data Engineer to design, build, and optimize our data platforms so teams across the company can make fast, reliable, data-driven decisions. You’ll be a key technical leader, owning end-to-end data pipelines and modeling, and setting best practices around how we work with data.

You’ll work heavily with Databricks, Spark, SQL, and Python
, building scalable data solutions that power analytics, reporting, and data products. Experience in payments or financial services is a strong plus.

• Design and build data pipelines
• Develop, maintain, and optimize ETL/ELT pipelines on Databricks and Spark.
• Integrate data from multiple internal and external sources into a centralized data platform.
• Own data modeling & architecture
• Design and maintain robust data models (e.g., star/snowflake schemas, data vault, dimensional models) to support analytics and self-service BI.
• Establish and enforce data modeling standards and documentation.
• Ensure data quality, reliability, and performance
• Implement data quality checks
  , validation frameworks, and monitoring.
• Tune queries and jobs for performance and cost efficiency in Databricks and downstream systems.
• Collaborate and lead
• Partner with data analysts, data scientists, and product/engineering teams to understand data needs and translate them into technical solutions.
• Provide technical leadership and mentorship to other data engineers; help review designs and code.
• Governance & best practices
• Contribute to and refine our data governance
  , security, and access control practices.
• Drive best practices around version control, CI/CD for data, and code standards.

• 7+ years of professional experience as a Data Engineer, Software Engineer, or similar role.
• Strong hands-on experience with Databricks (or a very similar cloud data platform) including cluster management, jobs, and notebooks.
• Advanced experience with Apache Spark for batch and/or streaming data processing.
• Expert-level SQL skills (complex joins, window functions, query optimization).
• Strong Python skills for data engineering (e.g., PySpark, data processing libraries, scripting).
• Proven experience in data modeling and designing schemas for analytics and reporting.
• Experience building and maintaining data pipelines in a cloud environment (AWS, Azure, or GCP).
• Strong understanding of data warehousing concepts
  , ETL/ELT best practices, and data lifecycle.
• Solid software engineering fundamentals: version control (git), testing, code reviews, and CI/CD.
• Excellent communication skills and the ability to collaborate with technical and non-technical stakeholders.

• Experience in payments, fintech, banking, or broader financial services (e.g., transaction data, ledgers, risk, fraud, reconciliation).
• Experience with streaming technologies (e.g., Spark Structured Streaming, Kafka, Kinesis, or similar).
• Familiarity with dbt or similar transformations-as-code frameworks.
• Experience with orchestration tools (e.g., Airflow, Databricks Workflows).
• Knowledge of BI tools (e.g., Power BI, Tableau, Looker) and how data models power them.
• Exposure to machine learning workflows and supporting data science teams.
• Experience implementing data governance, lineage, and catalog tools
  .

• A product mindset: you think about the end users of data and build with usability in mind.
• A bias for automation, reliability, and scalability over one-off solutions.
• Comfort with ambiguity, ownership of complex problems, and a desire to continuously improve the data ecosystem.

• 401k investment opportunity, with company match
• Medical, Dental, and Vision Plans
• Paid Time Off
• Paid Parental Leave
• Paid Volunteer Leave
• Fully Remote Work environment

• Full-Time, Remote in the United…