Principal, Cloud Data Platform Engineer

Position : Principal, Cloud Data Platform Engineer

Position Overview

The Principal - Cloud Data Platform Engineer is responsible for designing, building, and maintaining scalable, secure, and high-performance cloud-based data platforms that support analytics, machine learning, and enterprise data needs. This role ensures that data solutions are optimized for performance, cost, and reliability, while meeting governance, compliance, and business requirements. The engineer collaborates closely with data scientists, analysts, architects, and business stakeholders to deliver modern data capabilities on cloud platforms. Should have deep technical expertise, strong design judgment, and the ability to influence without authority.

Key Responsibilities

Platform Engineering & Development

• Design, implement, and maintain cloud-native data platforms (e.g., AWS, Azure).

• Develop and optimize data pipelines for ingestion, transformation, and processing of structured, semi-structured, and unstructured data.

• Build reusable frameworks and automation for data workflows, monitoring, and scaling.

• Ensure data storage and compute solutions are optimized for performance, security, and cost efficiency.

• Integrate cloud data services with enterprise applications, APIs, and third-party tools.

Data Management & Governance

• Implement data quality, lineage, and cataloging solutions across the data platform.

• Enforce governance, security, and compliance standards (e.g., GDPR, HIPAA, SOC2).

• Work with architects and security teams to implement role-based access, encryption, and audit controls.

• Support metadata management, data discoverability, and enterprise-wide data democratization.

Operations & Reliability

• Manage the operational health of the cloud data platform, including monitoring, alerting, and troubleshooting.

• Establish CI/CD pipelines for data services and platform components.

• Implement disaster recovery, backup, and failover strategies.

• Continuously optimize platform performance, cost, and scalability.

Collaboration & Innovation

• Partner with data scientists, BI teams, and application developers to enable advanced analytics and AI/ML workloads.

• Evaluate emerging cloud data services and recommend adoption based on enterprise needs.

• Provide technical expertise in designing cloud-based data architectures and solutions.

• Mentor junior engineers and contribute to best practices in cloud engineering and data development.

Qualifications

Education & Experience:

• Bachelor’s degree in Computer Science, Data Engineering, or related field (Master’s preferred).

• 15+ years of total work experience in technology infrastructure.

• 10+ years of experience in data engineering, cloud engineering, or platform engineering.

• Hands-on experience with at least one major cloud provider (AWS or Azure).

• Strong experience with big data and data processing frameworks (e.g., Spark, Databricks, Flink).

• Proven expertise in building data pipelines with tools like Apache Airflow, dbt, Kafka, or similar.

Skills & Competencies:

• In-depth knowledge and experience with Snowflake, Databricks, or similar cloud data platforms.

• Extensive knowledge and real hands-on experience of AI/ML data pipelines and feature engineering.

• Exposure to regulated environments (financial services, healthcare, etc.).

• Proficiency in SQL and one or more programming languages (Python, Java, or Scala).

• Deep understanding of cloud-native data services (e.g., AWS Redshift, Glue, S3, Athena; Azure Synapse, Data Factory; GCP BigQuery, Dataflow).

• Familiarity with containerization and orchestration (Docker, Kubernetes).

• Experience with infrastructure-as-code (Terraform, CloudFormation, Pulumi).

• Knowledge of FinOps principles for cost-efficient data platform design.

• Strong problem-solving, communication, and cross-functional collaboration skills.

• Excellent communication, mentorship, and ability to influence cross-functional teams.

Key Success Metrics

• Uptime, performance, and reliability of the cloud data platform.

• Data availability, quality, and accessibility for business and analytics use cases.

• Efficiency and scalability of data pipelines and processing.

• Cost optimization and resource utilization of cloud data services.

• Adoption and satisfaction of data platform services by business and analytics teams

Success Indicators

• Stable, scalable, and secure data platforms running in production.

• Reduced data incidents and faster root‑cause resolution.

• Improved developer productivity through reusable frameworks and standards.

• Strong technical mentorship and uplift of data engineering capabilities.

• Trusted technical advisor to architecture, security, and business leadership.