Data Architect

Responsibilities:

• Define and own the enterprise data architecture strategy, principles, standards, and technology roadmap.
• Design scalable and highly available data platforms, data lakes, data warehouses, and lakehouse architectures.
• Develop architecture for batch and real-time data ingestion, processing, transformation, and integration.
• Evaluate and recommend appropriate data technologies, platforms, frameworks, and tools based on business and technical requirements.
• Design robust data models, including conceptual, logical, and physical data models for operational and analytical workloads.
• Drive implementation of ETL/ELT pipelines, data integration frameworks, and data quality solutions.
• Architect solutions across cloud data platforms such as AWS/Azure/GCP and modern data technologies.
• Define data architecture patterns for analytics, reporting, Data Science, AI/ML, and GenAI use cases.
• Establish standards for data governance, security, privacy, metadata management, data lineage, and access control.
• Work closely with Data Engineering, Data Science, Product, Application Engineering, and Business teams to ensure alignment between business objectives and data architecture.
• Identify opportunities to improve data performance, scalability, reliability, cost efficiency, and operational excellence.
• Lead architecture reviews and provide technical guidance for complex data engineering initiatives.
• Mentor senior engineers and architects and contribute to building a strong data engineering and architecture practice.
• Stay current with emerging technologies in cloud, distributed systems, data platforms, AI/ML, and GenAI and assess their applicability to the organisation.

Requirements:

• 14-18 years of overall experience in Data Engineering, Data Architecture, or related areas, with significant experience in architecture and technical leadership.
• Strong experience designing large-scale, distributed data platforms and enterprise data architectures.
• Strong understanding of data modelling, data warehousing, data lakes, lakehouse architecture, and distributed data processing.
• Hands-on experience with technologies such as Spark, Kafka, Airflow, Databricks, Snowflake, BigQuery, Redshift, or equivalent platforms.
• Strong programming experience in Python, Java, or Scala.
• Strong knowledge of SQL and database technologies, including relational and NoSQL databases.
• Experience with cloud platforms, preferably AWS, with a strong understanding of cloud-native data services.
• Experience designing real-time and batch data processing architectures.
• Strong understanding of microservices, APIs, distributed systems, and event-driven architectures.
• Experience with data governance, security, quality, lineage, metadata, and compliance.
• Strong understanding of data architecture patterns and best practices for scalability, availability, performance, and cost optimisation.
• Experience working with CI/CD, DevOps, infrastructure automation, and observability for data platforms.
• Excellent problem-solving, communication, stakeholder management, and technical leadership skills.

Good to Have:

• Experience architecting data platforms for AI/ML and Generative AI applications.
• Knowledge of Vector Databases, RAG architectures, Knowledge Graphs, and AI data platforms.
• Experience with Data Mesh, Data Fabric, or modern lakehouse architectures.
• Experience with technologies such as Delta Lake, Iceberg, dbt, Kubernetes, Terraform, or equivalent.
• Experience in building enterprise-scale platforms in a high-growth product/SaaS environment.
• Prior experience leading architecture initiatives across multiple engineering teams.
• Strong architecture and system-design mindset with the ability to balance long-term strategy and immediate business needs.
• Ability to simplify complex data problems and design scalable, pragmatic solutions.
• Strong ownership and ability to influence technical decisions across teams.
• A hands-on technology leader who can design, review, and guide implementation rather than working purely at a conceptual level.
• Strong focus on engineering excellence, reliability, security, and measurable business impact.