Site Reliability Engineer II (Apache Spark, Python, AWS/GCP)

Job Description

How will you make an impact in this role?

We are seeking an experienced Site Reliability Engineer to join our Big Data infrastructure team. This role focuses on ensuring the reliability, scalability, and performance of our Apache Spark-based data processing systems and broader big data ecosystem. The ideal candidate will have 5+ years of hands-on experience with distributed systems, data platforms, and SRE practices.

Key Responsibilities:

Infrastructure Management & Reliability

1. Design, implement, and maintain highly available Apache Spark clusters and big data infrastructure across cloud and on-premises environments
2. Monitor and optimize performance of distributed data processing workloads, ensuring SLA compliance and minimal downtime
3. Implement comprehensive monitoring, alerting, and observability solutions for big data pipelines and infrastructure components
4. Lead incident response and post-mortem analysis for data platform outages, implementing preventive measures to avoid recurrence

Automation & Operations

1. Develop and maintain Infrastructure as Code (IaC) solutions using tools like Terraform, Ansible, or CloudFormation for big data infrastructure provisioning
2. Build automated deployment pipelines and CI/CD workflows for Spark applications and data platform components
3. Create and maintain runbooks, operational procedures, and disaster recovery plans for critical data systems
4. Implement capacity planning and auto-scaling solutions to handle varying data processing workloads efficiently

Platform Engineering & Optimization

1. Collaborate with data engineering teams to optimize Spark job configurations, cluster sizing, and resource allocation
2. Design and implement data platform governance, security, and compliance measures
3. Evaluate and integrate new big data technologies and tools to improve platform capabilities and performance
4. Establish best practices for code deployment, configuration management, and system maintenance

Required Skills and Experience:

Technical Expertise

1. 5+ years of experience in Site Reliability Engineering, DevOps, or similar roles with focus on distributed systems
2. Deep hands-on experience with Apache Spark (Scala, Python/PySpark) and Spark cluster management (YARN, Kubernetes, or standalone)
3. Proficiency with big data ecosystem technologies including Hadoop, HDFS, Hive, Kafka, Airflow, and data lakes/warehouses
4. Strong experience with cloud platforms (AWS, GCP, or Azure) and their big data services (EMR, Dataproc, HDInsight, etc.)
5. Advanced knowledge of containerization technologies (Docker, Kubernetes) and orchestration in data processing contexts

Infrastructure & Monitoring

1. Experience with infrastructure monitoring and observability tools (Prometheus, Grafana, ELK stack, Datadog, or similar)
2. Proficiency in Infrastructure as Code tools (Terraform, CloudFormation, Ansible) for managing big data infrastructure
3. Strong Linux/Unix system administration skills and experience with configuration management tools
4. Knowledge of networking, security, and performance tuning in distributed computing environments

Programming & Automation

1. Proficient in at least one programming language (Python, Scala, Java, or Go) for automation and tooling development
2. Experience with CI/CD pipelines and version control systems (Git, Jenkins, GitLab CI, or similar)
3. Strong scripting skills (Bash, Python) for automation and operational tasks
4. Understanding of software engineering best practices including testing, code review, and documentation

Preferred Qualifications

1. Experience with stream processing frameworks (Kafka Streams, Apache Flink, or Spark Streaming)
2. Knowledge of data governance, data quality, and data lineage tools
3. Familiarity with machine learning operations (MLOps) and model deployment at scale
4. Experience with database technologies (SQL, NoSQL) and data warehouse solutions
5. Relevant certifications in cloud platforms or big data technologies