Data architect

Overview

Design, govern and implement end-to-end data architectures on AWS for Barclays' Location Strategy projects, balancing technology risk against business delivery.

What you'll do

• Design and develop solutions as evolving products aligned with modern software engineering practices and automated delivery tooling, including identifying the technologies and platforms to use.
• Apply an appropriate workload placement strategy and maximise cloud capabilities such as elasticity, serverless and containerisation.
• Produce best-practice designs incorporating security principles such as defence in depth and reduction of blast radius, meeting the Bank's resiliency expectations.
• Balance risks and controls to deliver the agreed business and technology value, adopting standardised solutions where they fit and feeding into their evolution where they do not.
• Support operational teams with fault finding and performance issues, leveraging available tooling.
• Assess solution design impact in terms of risk, capacity and cost, including estimation of project change and ongoing run costs.
• Develop the architecture inputs required for the Bank's governance processes, including design artefacts for architecture, privacy, security and records management.
• Define lakehouse and distributed data processing architectures leveraging Apache Spark and Airflow on AWS (e.g. EMR, Glue).
• Establish logical and physical data models optimised for analytics and large-scale data processing.
• Ensure architectural alignment with enterprise data standards and cloud best practices.
• Partner with data engineering teams to define Spark-based processing patterns for batch and large-scale transformations.
• Guide development of Python-based data pipelines for ingestion, transformation and orchestration.
• Support optimisation of SQL-based analytics, including complex queries, performance tuning and consumption-layer design.
• Advise and influence decisions, contribute to policy development, and lead a team on complex tasks, setting objectives and coaching employees.

What you'll need

• Bachelor's degree in computer science, engineering or a related discipline.
• Experience designing cloud-based data platforms in enterprise environments.
• Hands-on experience with Apache Spark and Airflow for large-scale data processing and transformation.
• Strong programming experience in Python for data engineering and pipeline development.
• Advanced SQL skills, including analytical queries, performance optimisation, and data validation and reconciliation.
• Solid understanding of data modelling, distributed data processing and analytics workloads.

Nice to have

• AWS certifications.
• Deep knowledge of AWS data services: S3, Glue, Athena, Redshift, EMR, Lake Formation.
• Experience designing batch and analytical processing architectures using Spark and SQL.
• Astronomer and Databricks knowledge.
• Understanding of data ingestion, hydration, curation and consumption patterns.
• Proven experience in enterprise data architecture and governance.
• Strong understanding of data quality, lineage, metadata and security controls.
• Experience in regulated environments (banking or financial services).
• Ability to translate business requirements into scalable Spark and SQL-based data solutions.
• Strong stakeholder communication and architecture documentation skills.
• Comfort operating across architecture, engineering and delivery teams.

Details

• Based in Pune.