Senior DevOps + Data Engineer
2 days ago
Key Responsibilities
DevOps & Infrastructure:
- Design, implement, and manage CI/CD pipelines for multiple environments.
- Automate provisioning, deployment, and scaling of infrastructure across AWS, GCP, or Azure.
- Implement monitoring, alerting, and logging frameworks using tools such as Prometheus, Grafana, ELK Stack, or CloudWatch.
- Ensure high availability, disaster recovery, and adherence to infrastructure security best practices.
- Manage containerization and orchestration using Docker, Kubernetes, and Helm.
Data Engineering:
- Design, build, and optimize data pipelines for batch and real-time processing.
- Work with ETL frameworks such as Airflow, dbt, or AWS Glue to ensure reliable data flow and transformation.
- Collaborate with data scientists and analysts to maintain data quality, lineage, and governance.
- Optimize data storage and retrieval using cloud data warehouses like BigQuery, Redshift, or Snowflake.
- Integrate diverse data sources and maintain APIs or microservices for data exchange.
Collaboration & Strategy:
- Partner with development, analytics, and infrastructure teams to improve deployment velocity and system reliability.
- Define best practices, standards, and guidelines for DevOps and data operations.
- Mentor junior engineers and promote a culture of automation, reliability, and data excellence.
Required Skills & Experience:
- 10+ years of experience in DevOps, Infrastructure Automation, or related roles.
- Proven experience with cloud platforms (AWS, GCP, or Azure).
- Strong expertise in Infrastructure as Code (IaC) using Terraform, CloudFormation, or Pulumi.
- Proficiency in scripting and automation using Python, Bash, or Go.
- Hands-on experience with CI/CD tools (GitHub Actions, Jenkins, GitLab CI, CircleCI, etc.).
- Experience managing Kubernetes clusters and microservice-based architectures.
- Strong understanding of data pipelines, ETL tools, and data orchestration frameworks.
- Familiarity with data modeling, warehousing, and streaming technologies (Kafka, Spark, Flink, etc.).
- Solid knowledge of monitoring, observability, and security principles.
- Excellent problem-solving, communication, and leadership abilities.
Good to Have:
- Experience with machine learning pipelines and MLOps frameworks.
- Exposure to serverless architectures (AWS Lambda, Google Cloud Functions, etc.).
- Knowledge of cost optimization and performance tuning in cloud environments.
- Contributions to open-source projects or technical blogs/publications.
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