Data Operations Engineer, Trulioo

• We are looking for a detail-oriented and innovative Data Operations Engineer to join our team
• The ideal candidate is passionate about data, enjoys solving complex problems, and thrives in a fast-paced environment
• Bachelor’s Degree in Computer Science, Information Technology, Data Engineering, or a related field (or equivalent practical experience)
• 3+ years of experience in a data operations, data engineering, or IT infrastructure role
• Technical proficiency with cloud platforms (AWS) and data pipeline technologies (e.g., Apache Airflow, or similar)
• Proficiency in programming languages Python, SQL, and familiarity with tools like Spark, Hadoop, or Kafka
• Experience with databases (relational and non-relational) such as MySQL, PostgreSQL, MongoDB, DynamoDB, Cassandra, or Snowflake
• Strong problem-solving skills, with an ability to troubleshoot and resolve issues in real-time
• Familiarity with CI/CD pipelines, automation, and infrastructure-as-code tools like Terraform or Kubernetes
• Knowledge of data governance, compliance frameworks, and best practices for ensuring security and integrity of data
• Excellent communication skills, with the ability to work collaboratively with cross-functional teams

Desirable

• Reporting to the Manager, Data Science, you will be responsible for ensuring the smooth, reliable, and efficient operation of our data analytics infrastructure to support our growing internal analytics and reporting ecosystems
• You will manage data pipelines, optimize data workflows, monitor data quality, and collaborate closely with data engineering, analytics, and IT teams to support data-driven initiatives
• Data Pipeline Management: Design, build, and maintain data pipelines to ensure reliable data ingestion, transformation, and storage from various sources
• Monitoring & Troubleshooting: Continuously monitor data workflows and infrastructure to identify and resolve performance bottlenecks, data quality issues, and downtime
• Data Quality Assurance: Implement and manage processes for validating, auditing, and maintaining high data quality across all systems
• Automation & Optimization: Develop automated solutions to streamline data operations, including monitoring alerts, reporting, and data processing workflows
• Collaboration: Work closely with data engineers, data analysts, and IT to ensure data infrastructure meets business needs and scale as data demands grow
• Data Security & Compliance: Ensure data pipelines and storage systems comply with data privacy regulations and industry standards for security
• Documentation & Best Practices: Document data operation processes, workflows, and infrastructure, and advocate for best practices in data management and engineering
• Capacity Planning & Scaling: Assist in evaluating and scaling infrastructure to accommodate the growing volume of data and users, ensuring efficient resource allocation