[Hiring] Data Security Engineer @Oportun

Role Description

The ideal candidate will focus on the Data Engineering aspects of the role, specifically within the context of Security-related data. We are looking for someone who can build the pipelines and infrastructure necessary to handle security logs, threat intelligence, and sensitive data sets.

The Data Security Engineer supports the design, implementation, and maintenance of scalable data systems and pipelines that enable Oportun’s data-driven decision-making. Operating under established data architecture standards and governance frameworks, this role helps ensure the reliability, quality, and accessibility of data across platforms supporting Oportun’s mission.

The engineer collaborates with data, cloud, and product teams to build and optimize data pipelines, implement data models, and support analytics and machine learning use cases. This includes:

• Developing data processing solutions
• Maintaining data infrastructure
• Automating workflows to improve efficiency and reduce operational risk

As a developing professional, the Data Security Engineer focuses on execution and delivery of scoped data initiatives with guidance from more experienced engineers, progressively building technical depth and system ownership.

Responsibilities

• Design, implement, and maintain scalable data pipelines and data processing systems aligned with Oportun’s data architecture standards.
• Collaborate with data scientists, analysts, and engineering teams to integrate data solutions into applications, analytics platforms, and workflows.
• Develop and optimize ETL/ELT processes using modern data platforms such as Databricks.
• Write efficient and scalable code using Python and SQL for data transformation, validation, and ingestion.
• Ensure data quality, consistency, and reliability through validation, monitoring, and testing practices.
• Support data platform monitoring and troubleshooting by investigating pipeline failures and contributing to root cause analysis.
• Automate repetitive data workflows to improve efficiency and scalability across data operations.
• Contribute to the documentation of data pipelines, data models, and system architecture.
• Partner with governance and compliance teams to ensure alignment between data practices and organizational standards.
• Perform other engineering-related tasks and initiatives as assigned within the Data Engineering function.

Requirements

• 2–4 years of experience in data engineering, software engineering, or related technical roles.
• Strong proficiency in SQL and Python programming for data processing and transformation.
• Hands-on experience with Databricks or similar distributed data processing platforms (e.g., Spark).
• Foundational understanding of data modeling, ETL/ELT concepts, and data warehousing principles.
• Experience working with cloud platforms (AWS, GCP, or Azure) and data storage solutions.
• Familiarity with version control and collaboration tools (e.g., GitHub, Jira, Confluence).
• Strong problem-solving and critical-thinking skills; ability to work with large datasets and optimize performance.

Preferred

• Bachelor’s degree in Computer Science, Data Engineering, or a related technical field.
• Experience with big data technologies (e.g., Apache Spark, Delta Lake).
• Exposure to workflow orchestration tools (e.g., Airflow, Databricks Workflows).
• Experience with infrastructure-as-code tools (e.g., Terraform).
• Understanding of data governance, data quality frameworks, and security best practices.
• Relevant certifications in cloud or data engineering (e.g., Databricks, AWS, GCP).