CoffeeBeans

Job Overview:

We are seeking a highly skilled Data Analyst with 6+ years of experience in analytics, data modeling, and advanced SQL. The ideal candidate has strong expertise in building scalable data models using dbt, writing efficient Python scripts, and delivering high-quality insights that support data-driven decision-making.

Key Responsibilities

• Design, develop, and maintain data models using dbt (Core and dbt Cloud).
• Build and optimize complex SQL queries to support reporting, analytics, and data pipelines.
• Write Python scripts for data transformation, automation, and analytics workflows.
• Ensure data quality, integrity, and consistency across multiple data sources.
• Collaborate with cross-functional teams (Engineering, Product, Business) to understand data needs.
• Develop dashboards and reports to visualize insights (using tools such as Tableau, Looker, or Power BI).
• Perform deep-dive exploratory analysis to identify trends, patterns, and business opportunities.
• Document data models, pipelines, and processes.
• Contribute to scaling the analytics stack and improving data architecture.

Required Qualifications

• 6 - 9 years of hands-on experience in data analytics or data engineering.
• Expert-level skills in SQL (complex joins, window functions, performance tuning).
• Strong experience building and maintaining dbt data models.
• Proficiency in Python for data manipulation, scripting, and automation.
• Solid understanding of data warehousing concepts (e.g., dimensional modeling, ELT/ETL pipelines).
• Understanding with cloud data platforms (Snowflake, BigQuery, Redshift, etc.).
• Strong analytical thinking and problem-solving skills.
• Excellent communication skills with the ability to present insights to stakeholders.
• Trino and lakehouse architecture experience good to have

About the role:

We are seeking an experienced DevOps Engineer with deep expertise in Jenkins, Docker, Ansible, and Kubernetes to architect and maintain secure, scalable infrastructure and CI/CD pipelines. This role emphasizes security-first DevOps practices, on-premises Kubernetes operations, and integration with data engineering workflows.

🛠 Required Skills & Experience

Technical Expertise

• Jenkins (Expert): Advanced pipeline development, DSL scripting, security integration, troubleshooting
• Docker (Expert): Secure multi-stage builds, vulnerability management, optimisation for Java/Scala/Python
• Ansible (Expert): Complex playbook development, configuration management, automation at scale
• Kubernetes (Expert - Primary Focus): On-premises cluster operations, security hardening, networking, storage management
• SonarQube/Code Quality (Strong): Integration, quality gate enforcement, threshold management
• DevSecOps (Strong): Security scanning, compliance automation, vulnerability remediation, workload governance
• Spark ETL/ETA (Moderate): Understanding of distributed data processing, job configuration, runtime behavior

Core Competencies

• Deep understanding of DevSecOps principles and security-first automation
• Strong troubleshooting and problem-solving abilities across complex distributed systems
• Experience with infrastructure-as-code and GitOps methodologies
• Knowledge of compliance frameworks and security standards
• Ability to mentor teams and drive best practice adoption

🎓Qualifications

• 6 - 10 Years years of hands-on DevOps
• Proven track record with Jenkins, Docker, Kubernetes, and Ansible in production environments
• Experience managing on-premises Kubernetes clusters (bare-metal preferred)
• Strong background in security hardening and compliance automation
• Familiarity with data engineering platforms and big data technologies
• Excellent communication and collaboration skills

🚀 Key Responsibilities

1.CI/CD Pipeline Architecture & Security

• Design, implement, and maintain enterprise-grade CI/CD pipelines in Jenkins with embedded security controls:
• Build greenfield pipelines and enhance/stabilize existing pipeline infrastructure
• Diagnose and resolve build, test, and deployment failures across multi-service environments
• Integrate security gates, compliance checks, and automated quality controls at every pipeline stage
• Manage and optimize SonarQube and static code analysis tooling:
• Enforce code quality and security scanning standards across all services
• Maintain organizational coding standards, vulnerability thresholds, and remediation workflows
• Automate quality gates as integral components of CI/CD processes
• Engineer optimized Docker images for Java, Scala, and Python applications:
• Implement multi-stage builds, layer optimization, and minimal base images
• Conduct image vulnerability scanning and enforce compliance policies
• Apply containerization best practices for security and performance
• Develop comprehensive Ansible automation:
• Create modular, reusable, and secure playbooks for configuration management
• Automate environment provisioning and application lifecycle operations
• Maintain infrastructure-as-code standards and version control

2.Kubernetes Platform Operations & Security

• Lead complete lifecycle management of on-premises/bare-metal Kubernetes clusters:
• Cluster provisioning, version upgrades, node maintenance, and capacity planning
• Configure and manage networking (CNI), persistent storage solutions, and ingress controllers
• Troubleshoot workload performance, resource constraints, and reliability issues
• Implement and enforce Kubernetes security best practices:
• Design and manage RBAC policies, service account isolation, and least-privilege access models
• Apply Pod Security Standards, network policies, secrets encryption, and certificate lifecycle management
• Conduct cluster hardening, security audits, monitoring, and policy governance
• Provide technical leadership to development teams:
• Guide secure deployment patterns and containerized application best practices
• Establish workload governance frameworks for distributed systems
• Drive adoption of security-first mindsets across engineering teams

3.Data Engineering Support

• Collaborate with data engineering teams on Spark-based workloads:
• Support deployment and operational tuning of Spark ETL/ETA jobs
• Understand cluster integration, job orchestration, and performance optimization
• Debug and troubleshoot Spark workflow issues in production environments

We are seeking an experienced Lead DevOps Engineer with deep expertise in Kubernetes infrastructure design and implementation. This role requires someone who can architect, build, and manage enterprise-grade Kubernetes clusters from the ground up. You’ll lead modernization initiatives, shape infrastructure strategy, and work with cutting-edge cloud-native technologies.

🚀 Key Responsibilities

Infrastructure Design & Implementation

• Architect and design enterprise-grade Kubernetes clusters across AWS, Azure, and GCP.
• Build production-ready Kubernetes infrastructure with HA, scalability, and security best practices.
• Implement Infrastructure as Code with Terraform, Helm, and GitOps workflows.
• Set up monitoring, logging, and observability for Kubernetes workloads.
• Design and execute backup and disaster recovery strategies for containerized applications.

Leadership & Team Management

• Lead a team of 3–4 DevOps engineers, providing technical mentorship.
• Drive best practices in containerization, orchestration, and cloud-native development.
• Collaborate with development teams to optimize deployment strategies.
• Conduct code reviews and maintain infrastructure quality standards.
• Build knowledge-sharing culture with documentation and training.

Operational Excellence

• Manage and scale CI/CD pipelines integrated with Kubernetes.
• Implement security policies (RBAC, network policies, container scanning).
• Optimize cluster performance and cost-efficiency.
• Automate operations to minimize manual interventions.
• Ensure 99.9% uptime for production workloads.

Strategic Planning

• Define the infrastructure roadmap aligned with business needs.
• Evaluate and adopt new cloud-native technologies.
• Perform capacity planning and cloud cost optimization.
• Drive risk assessment and mitigation strategies.

🛠 Must-Have Technical Skills

Kubernetes Expertise

• 6+ years of hands-on Kubernetes experience in production.
• Deep knowledge of Kubernetes architecture (etcd, API server, scheduler, kubelet).
• Advanced Kubernetes networking (CNI, Ingress, Service mesh).
• Strong grasp of Kubernetes storage (CSI, PVs, StorageClasses).
• Experience with Operators and Custom Resource Definitions (CRDs).

Infrastructure as Code

• Terraform (advanced proficiency).
• Helm (developing and managing complex charts).
• Config management tools (Ansible, Chef, Puppet).
• GitOps workflows (ArgoCD, Flux).

Cloud Platforms

• Hands-on experience with at least 2 of the following:
• AWS: EKS, EC2, VPC, IAM, CloudFormation
• Azure: AKS, VNets, ARM templates
• GCP: GKE, Compute Engine, Deployment Manager

CI/CD & DevOps Tools

• Jenkins, GitLab CI, GitHub Actions, Azure DevOps
• Docker (advanced optimization and security practices)
• Container registries (ECR, ACR, GCR, Docker Hub)
• Strong Git workflows and branching strategies

Monitoring & Observability

• Prometheus & Grafana (metrics and dashboards)
• ELK/EFK stack (centralized logging)
• Jaeger/Zipkin (tracing)
• AlertManager (intelligent alerting)

💡 Good-to-Have Skills

• Service Mesh (Istio, Linkerd, Consul)
• Serverless (Knative, OpenFaaS, AWS Lambda)
• Running databases in Kubernetes (Postgres, MongoDB operators)
• ML pipelines (Kubeflow, MLflow)
• Security tools (Aqua, Twistlock, Falco, OPA)
• Compliance (SOC2, PCI-DSS, GDPR)
• Python/Go for automation
• Advanced Shell scripting (Bash/PowerShell)

🎓 Qualifications

• Bachelor’s in Computer Science, Engineering, or related field.
• Certifications (preferred):
• Certified Kubernetes Administrator (CKA)
• Certified Kubernetes Application Developer (CKAD)
• Cloud provider certifications (AWS/Azure/GCP).

Experience

• 6–7 years of DevOps/Infrastructure engineering.
• 4+ years of Kubernetes in production.
• 2+ years in a lead role managing teams.
• Experience with large-scale distributed systems and microservices.