Data Architect

Role & 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 organization.

Preferred candidate profile

• 13-15 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 modeling, 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 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 optimization.

• Experience working with CI/CD, DevOps, infrastructure automation, and observability for data platforms.

• Excellent problem-solving, communication, stakeholder management, and technical leadership skills.

Candidates from Product companies who can join within 30 days need only apply. Candidate meeting the requirement can share profile at [email protected]