Postúlate en Kit Empleo: kitempleo.com.co/empleo/1asm7p

Requisition ID: 225636

Thanks for your interest in ScotiaTech, Scotiabank's new and innovative Technology hub in Bogota.

Join a purpose driven winning team that promotes creativity and innovation in a fast-paced environment, where we’re always committed to results, in an inclusive, diverse, and high-performing culture.

**Purpose**

The DataOps Engineer is responsible for building, maintaining, and optimizing scalable and reliable data pipelines and infrastructure in a cloud environment, under the guidance of the Senior DataOps Engineer. This role collaborates with a integral team of DataOps Engineers and cross-functional stakeholders to ensure high-quality data products and services that support the organization’s data-driven goals. By implementing DataOps best practices and leveraging cloud technologies, the DataOps Engineer contributes to efficient data workflows, operational reliability, and seamless delivery across multiple countries.

**Accountabilities**
- **Data Pipeline Development and Maintenance**:

- Design, develop, and maintain cloud-based data pipelines using platforms such as Azure, GCP or AWS (e.g., Dataflow, BigQuery, GCS, S3, Lambda functions, Cloud Functions, Composer).
- Implement automated data workflows, including ETL processes, data quality checks, and orchestration using tools like Python, Spark, Airflow or Cloud Composer.
- Ensure pipelines meet performance and reliability targets (e.g., 99.5% uptime, <1% error rate) as defined by SLAs.
- **Cloud Infrastructure Support**:

- Deploy and manage cloud data infrastructure components, ensuring scalability, security, and cost efficiency under the Senior DataOps Engineer’s direction.
- Monitor and optimize cloud resource usage (e.g., storage, compute) using tools like Azure Cost Management, or GCP Billing.
- **Automation and CI/CD Implementation**:





- Build and maintain CI/CD pipelines for data workflows using tools such as Jenkins, GitHub Actions, Cloud Build and Jfrog to enable rapid and reliable deployments.
- Automate repetitive tasks, such as data validation and monitoring, to improve operational efficiency.
- **Collaboration and Global Teamwork**:

- Work closely with Data Engineers, Data Scientists, and business teams to deliver data products that meet business requirements.
- Collaborate with global DataOps team members across multiple time zones, contributing to shared goals and knowledge sharing.
- Provide regular updates on task progress and escalate issues to the Senior DataOps Engineer as needed.
- **Monitoring and Incident Response**:

- Monitor data pipelines and infrastructure using tools like Cloud Monitoring, Prometheus, or Cloud Logging to ensure operational health.
- Participate in incident response and troubleshooting, conducting root cause analysis to resolve issues within defined SLAs (e.g., <4 hours for critical incidents).
- **Compliance and Security**:

- Implement data security measures, such as encryption to Personal Identifiable Information and role-based access control (RBAC), in line with global regulations (e.g., GDPR, CCPA).
- Support audits and documentation to ensure compliance with organizational and regulatory standards.
- **Control versioning**:





- Deploy and manage repositories using git to keep and track changes on time
- **Continuous Improvement**:

- Contribute to the development of reusable frameworks, scripts, and documentation to enhance team efficiency.
- Stay informed about DataOps and cloud technology trends, proposing improvements to processes and tools.

**Education / Experience / Other Information (include only those that are specific to the role)**
- **Education**:

- Bachelor’s degree in computer science, Data Engineering, Information Technology, or a related field.
- **Experience**:

- Minimum of 3 years of experience in data engineering, DevOps, or DataOps roles, with hands-on experience in cloud environments (e.g., AWS, Azure, GCP).
- Proven experience building and maintaining data pipelines using tools like Dataflow, Spark, or BigQuery.
- Familiarity with CI/CD pipelines and automation tools (e.g., Jenkins, GitHub Actions, Cloud Build).
- Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation) and monitoring solutions (e.g., Cloud Monitoring, Prometheus).
- Exposure to working in distributed teams or cross-functional environments is preferred.
- 3-5 years using Python and SQL
- **Certifications (preferred)**:

- Cloud certifications such as Google Cloud Associate Data Engineer, Azure Data Fundamentals, AWS Certified Developer.
- Entry-level DevOps or Data Ops certifications are a plus.
- **Skills**:

- Proficiency in programming languages like Python, SQL, or Scala for data pipeline development.
- Proficiency in orchestration tools like Airflow, Rancher, Kubernetes, Terraform.
- Solid understanding of Data Ops principles, including automation, ETL and ELT, version control, and observability.
- Strong problem-solving skills and attention to detail in managing c

Postúlate en Kit Empleo: kitempleo.com.co/empleo/1asm7p

📌 Data Engineer (Bogotá)
🏢 Scotiabank
📍 Bogotá