Data Platform Architect

Role & responsibilities

• Design and lead the architecture of scalable, secure, and high-performance enterprise data platforms.
• Define data platform architecture, technology standards, reference architectures, and integration patterns.
• Design and optimize data lakes, data warehouses, lakehouses, and modern cloud data platforms.
• Evaluate and implement cloud technologies across AWS, Azure, or GCP based on business and technical requirements.
• Lead the design of batch and real-time data pipelines using technologies such as Spark, Kafka, and modern ETL/ELT platforms.
• Ensure data platforms are scalable, highly available, reliable, cost-efficient, and secure.
• Establish data governance, data quality, metadata management, lineage, privacy, and security frameworks.
• Collaborate with Data Engineering, Data Science, Analytics, Product, Security, and Business teams to translate requirements into technical solutions.
• Develop architecture documentation, technical standards, solution designs, and technology roadmaps.
• Identify and resolve platform performance, scalability, reliability, and integration challenges.
• Drive modernization and migration initiatives from legacy data platforms to cloud and lakehouse architectures.
• Provide technical leadership and mentorship to data engineers and architecture teams.
• Stay current with emerging technologies in cloud, data engineering, AI/ML, and distributed systems.

Preferred candidate profile

• 5 to 10 years of experience in Data Engineering, Data Architecture, Cloud Architecture, or related technology roles.
• Proven experience designing and implementing large-scale data platforms and distributed data systems.
• Strong hands-on expertise with at least one major cloud platform: AWS, Azure, or GCP.
• Strong knowledge of data warehousing, data lakes, lakehouse architecture, ETL/ELT, data modelling, and distributed computing.
• Proficiency in SQL and at least one programming language such as Python, Java, or Scala.
• Experience with technologies such as Apache Spark, Kafka, Databricks, Snowflake, BigQuery, Redshift, or equivalent platforms.
• Strong understanding of data governance, security, privacy, metadata, lineage, and data quality.
• Experience with DevOps, CI/CD, Infrastructure as Code, containerization, and cloud-native architecture.
• Strong understanding of system design, scalability, high availability, disaster recovery, and performance optimization.
• Excellent problem-solving, communication, stakeholder management, and technical leadership skills.
• Ability to translate business requirements into scalable and maintainable technical architectures.
• Bachelor's degree in Computer Science, Information Technology, Engineering, Data Engineering, or a related technical discipline.
• Cloud, data engineering, or architecture certifications such as AWS, Azure, GCP, Databricks, or Snowflake are an added advantage.