Data Engineer - Data Architecture

About the role

Design, develop and maintain data solutions for data generation, collection, and processing. Create data pipelines, ensure data quality, and implement ETL (extract, transform and load) processes to migrate and deploy data across systems. Lead the design and implementation of next-generation data platforms. Architect scalable, cloud-native, and real-time data ecosystems to support advanced analytics, AI/ML, and digital transformation initiatives.

Key responsibilities

• Lead end-to-end data architecture for large-scale modernization and digital transformation programs.
• Design and implement modern data platforms using cloud-native services (Azure, AWS, GCP) including lakehouses, streaming pipelines, and orchestration frameworks.
• Define and drive adoption of modern architecture patterns such as data mesh, data fabric, data virtualization, and knowledge graphs.
• Drive the adoption of modern data stacks: Spark, Databricks, Snowflake, Synapse, BigQuery, Kafka, dbt, Delta Lake, etc.
• Develop logical and physical data models to support structured, semi-structured, and unstructured data.
• Collaborate with data engineers, product owners, and business stakeholders to translate requirements into scalable data solutions.
• Define and implement strategies for data governance, metadata management, master data management, data security and access controls.
• Evaluate and recommend tools, platforms, and frameworks aligned with enterprise data strategy.

About you

• 10+ years of experience in data architecture and engineering roles.
• Hands-on experience with at least one major cloud platform (Azure, AWS, GCP) or emerging platforms like IOMETE.
• Expertise in modern data warehousing and lakehouse platforms: Snowflake, Databricks, Synapse, Redshift, BigQuery.
• Deep knowledge of data integration, ETL/ELT tools (e.g., dbt, Azure Data Factory, Glue, Airflow).
• Experience with streaming and real-time data platforms like Kafka, Spark Structured Streaming.
• Strong understanding of data modeling, data quality frameworks, and metadata management.
• Familiarity with data mesh, data fabric, and data product concepts.
• Experience with data cataloging and discovery tools: Collibra, Alation, Purview, etc.
• Knowledge of DevOps, DataOps, and CI/CD for data pipelines.
• Experience with AI/ML platform integration.