Data Platform Engineer

Role & responsibilities

• Own the strategy, architecture, development, and operations of the organization's data platform
• Lead and mentor data engineers, platform engineers, DevOps engineers, and other technical team members
• Design scalable, reliable, secure, and cost-efficient data platforms supporting analytics, BI, AI, and business applications
• Define the data platform roadmap in collaboration with data, engineering, product, security, and business leadership
• Manage data lakes, data warehouses, lakehouse platforms, ETL/ELT pipelines, and real-time data infrastructure
• Establish standards for data ingestion, transformation, storage, processing, orchestration, and data serving
• Drive cloud migration, platform modernization, scalability, and infrastructure optimization initiatives
• Ensure high availability, performance, reliability, monitoring, and disaster recovery of critical data-platform services
• Implement data security, access controls, encryption, privacy, governance, and compliance requirements
• Partner with data governance teams to improve data quality, lineage, cataloging, metadata management, and discoverability
• Manage platform capacity, infrastructure utilization, performance, and cloud costs
• Establish CI/CD, Infrastructure as Code, automated testing, deployment, and operational best practices
• Drive adoption of technologies such as Databricks, Snowflake, Spark, Kafka, Airflow, Kubernetes, and cloud-native data services
• Define and monitor platform SLAs, SLOs, KPIs, availability, pipeline performance, and operational health
• Lead incident management, root-cause analysis, problem resolution, and continuous improvement
• Evaluate new data and cloud technologies and make recommendations based on scalability, security, performance, and cost
• Manage technology vendors, external partners, budgets, and platform-related contracts where required
• Collaborate with senior leadership to align platform investments with business and technology priorities

Preferred candidate profile

• Bachelor's degree in Computer Science, Information Technology, Engineering, Data Engineering, or a related technical field
• 5 to 10 years of experience in data engineering, data platform engineering, cloud engineering, or technology infrastructure
• Proven experience leading data/platform engineering teams and enterprise-scale technology initiatives
• Strong hands-on knowledge of SQL, Python, PySpark, Apache Spark, ETL/ELT, and data modeling
• Strong experience with data lakes, data warehouses, lakehouse architecture, and distributed data-processing systems
• Expertise in one or more cloud platforms: AWS, Microsoft Azure, or Google Cloud
• Experience with platforms and technologies such as Databricks, Snowflake, Delta Lake, Kafka, Airflow, BigQuery, Synapse, or Redshift
• Strong understanding of cloud infrastructure, networking, IAM/RBAC, security, encryption, and access management
• Experience with Terraform/Infrastructure as Code, Docker, Kubernetes, Git, and CI/CD pipelines
• Strong understanding of data governance, data quality, metadata, lineage, cataloging, and regulatory requirements
• Experience with platform monitoring, observability, performance optimization, capacity planning, and disaster recovery
• Knowledge of cloud cost optimization and FinOps principles is desirable
• Strong understanding of DevOps, SRE, Agile, SDLC, and incident-management practices
• Excellent technical leadership, stakeholder management, communication, and team-management skills
• Ability to translate business and analytics requirements into scalable platform architecture and engineering solutions
• Strong problem-solving and decision-making skills with an emphasis on reliability, security, scalability, and operational excellence
• Relevant certifications such as AWS/Azure/GCP, Databricks, Snowflake, Kubernetes, or Terraform are advantageous
• M.Tech/MS in Computer Science, Data Engineering, or a related discipline is desirable for highly technical platform roles