Senior DevOps + Data Engineer

4 weeks ago

Ahmedabad India IT Merchant Full time

Job Description 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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