Site Reliability Engineer

We are seeking a skilled Site Reliability Engineer (SRE) with a strong background in data infrastructure and machine learning operations (MLOps). The ideal candidate will be responsible for designing and deploying scalable solutions for high-volume data, building robust ETL/ELT pipelines, and ensuring the reliability of our systems. You will play a critical role in automating workflows, managing releases, and collaborating with cross-functional teams to drive efficiency and innovation.

Key Responsibilities

• Data Infrastructure & Engineering: Design and develop scalable solutions for time-series data storage and retrieval. Build robust ETL/ELT pipelines using big data tools like Spark, Kafka, and Azure Data Factory.
• CI/CD & MLOps: Develop and manage CI/CD pipelines for ML models and data infrastructure using tools like MLflow, Jenkins, and Terraform.
• Monitoring & Automation: Implement end-to-end monitoring for infrastructure and data pipelines using Prometheus and Grafana. Automate workflows to ensure high availability and zero-downtime deployments.
• Release & Incident Management: Manage releases across environments, respond to Severity 1 incidents, and lead Root Cause Analysis (RCA).
• Collaboration & Analytics: Work with product teams to deploy releases, resolve customer escalations, and build tools that provide insights into business KPIs.

Qualifications

• A Bachelor's or Master's degree in Engineering with experience in SRE or related roles.
• Certifications such as AWS Solution Architect Associate or Azure are preferred.
• Strong technical skills in cloud platforms (AWS, Azure), big data technologies (Spark, Kafka, Databricks), and data storage.
• Proficiency in SQL, Python, Java/Scala, and container orchestration (Kubernetes).
• Familiarity with DevOps tools like Jenkins, Terraform, and Ansible.
• Excellent problem-solving, communication, and time management skills.

Competencies

• Problem-Solving & RCA: The ability to effectively troubleshoot and conduct root cause analysis.
• Technical Expertise: Strong skills in cloud and data engineering.
• Collaboration: Experience working with cross-functional teams.
• Automation: A mindset focused on innovation and automation to improve processes.
• Customer Focus: The ability to manage customer escalations and drive improvements.