Senior Cloud Native Data Platform Manager

Licentious Enterprises is revolutionizing the way data platforms are sourced, processed, and delivered.

Our obsession with ensuring the highest standards in data quality, chain logistics, and customer delight drives our pursuit of excellence.

We are looking for a seasoned Data Platform & DevOps Engineering Manager to lead the development and operations of modern cloud native data and infrastructure platforms.

• The successful candidate will drive the architecture and execution of large-scale data processing, analytics systems, and DevOps practices that enable high-quality insights and rapid product iteration.

This is a strategic and hands-on leadership role, managing a team of data engineers, DevOps specialists, and cloud platform engineers.

Data Platform & Engineering:

Architect, build, and maintain scalable and secure data infrastructure using tools like Apache Hadoop, Hive, Spark, Kafka, Airflow, and Delta Lake.

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.

Collaborate closely with Product, Engineering, and Data Science teams to deliver accurate, timely, and actionable data.

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.

DevOps & Platform Engineering:

Lead the design and implementation of robust CI/CD pipelines, version control, testing, and deployment practices.

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.

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.

Requirements:

• 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.
• Ensure platform security, data protection compliance, and cloud infra governance.
• Own platform reliability, incident management processes, incident retros, and on-call practices.
• Plan for infra scaling and performance benchmarking to support growing order volumes and data ingestion rates.
• Operational KPIs/OKRs will include infra uptime, pipeline latency, model deployment TAT, cloud cost optimization, and elevate security & privacy management.

Desirable Experience:

• 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.