Leading Cloud-Native Data and Infrastructure Platforms Engineer

Are you a seasoned Data Platform & DevOps Engineering Manager looking for a new challenge? We're seeking a highly skilled professional to lead the development and operations of our modern, cloud-native data and infrastructure platform.

About the Role :

Our ideal candidate will have a strong background in data engineering, DevOps, and infrastructure roles, with at least 3 years in a technical leadership or managerial capacity. They will be responsible for architecting, building, and maintaining scalable and secure data infrastructure using tools like Apache Hadoop, Hive, Spark, Kafka, Airflow, and Delta Lake.

• Data Platform & Engineering :
  • Develop robust ETL/ELT pipelines, data models, and streaming data workflows to support analytics, business intelligence, and machine learning use cases.
  • Optimize data storage and compute using cloud-native solutions (AWS S3, Redshift, EMR, Glue, Athena, etc.).
  • Integrate with modern data stack tools such as dbt, Snowflake, BigQuery, and Fivetran (or custom connectors).
  • Ensure data quality, lineage, cataloging, and observability using tools like Apache Atlas, Great Expectations, and Amundsen.
• ML & Advanced Analytics Enablement :
  • Support Data Science and AI/ML teams by maintaining model pipelines and training infrastructure.
  • Enable MLOps frameworks using MLflow, SageMaker, PyTorch, or TensorFlow for seamless experimentation and deployment.
  • Manage model versioning, metadata tracking, and real-time inference workflows.
• DevOps & Platform Engineering :
  • Lead the design and implementation of robust CI/CD pipelines, version control, testing, and deployment practices.
  • Implement Infrastructure as Code (IaC) using Terraform, Ansible, or Pulumi.
  • Manage containerization and orchestration platforms like Docker, Kubernetes (EKS preferred).
  • Own cloud infrastructure (preferably AWS), including networking, security, cost governance, and compliance.
  • Set up monitoring and alerting using Prometheus, Grafana, ELK Stack, or DataDog.
• Leadership & People Management :
  • Hire, coach, and mentor a team of 8-12 data, devops and platform engineers.
  • Set clear objectives, track performance, and build a culture of ownership, continuous learning, and innovation.
  • Collaborate cross-functionally to translate business needs into scalable engineering solutions.

Required Skills & Qualifications :

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 10+ years of experience in data engineering, DevOps, or infrastructure roles, with at least 3 years in a technical leadership or managerial capacity.
• Strong experience with cloud platforms (AWS preferred), distributed data systems, and large-scale batch + real-time data processing.
• Hands-on proficiency with tools like Kafka, Airflow, Hadoop, Hive, Spark, dbt, PyTorch, MLflow, and Docker/Kubernetes.
• Proven experience in building and maintaining enterprise data platforms and ML Ops pipelines.
• Strong understanding of CI/CD, GitOps, system monitoring, SRE, and cost optimization best practices.
• Exceptional problem-solving skills, stakeholder communication, and team leadership.
• Ensure platform security, data protection compliance, and cloud infra governance.

Incident Management / SRE Practices :

• Own platform reliability, incident management processes, incident retros, and on-call practices.

Infra Scale & Optimization Responsibilities :

• Plan for infra scaling and performance benchmarking to support growing order volumes and data ingestion rates.
• Operational KPIs/OKRs will include own infra uptime , pipeline latency , model deployment TAT , cloud cost optimization & Elevate Security & Privacy Management.

Nice to Have :

• Experience with data privacy regulations (GDPR, SOC2, etc.).
• Exposure to security best practices in DevOps and cloud infra.
• Familiarity with Data Mesh or Lakehouse architecture.