Data Architect

We are seeking a highly experienced Senior Data Architect with strong hands-on expertise in data analytics, Databricks on AWS, PySpark, Python, SQL, and ETL tools such as Informatica. This is a senior leadership role requiring strong technical ownership, proactive risk and issue management, and excellent communication skills.

The ideal candidate will have deep experience designing scalable, secure, and cost-efficient data solutions on AWS, migrating data assets from on-premises platforms to the cloud, and leveraging GenAI-driven automation to accelerate data modernisation initiatives.

Key Responsibilities

Design and implement scalable, secure, and cost-efficient data architecture solutions on AWS.

Lead data migration initiatives from on-premises environments to AWS.

Work hands-on with Databricks on AWS, PySpark, Python, and SQL to build data pipelines and analytical solutions.

Develop data models and deliver data-driven insights to support business decision-making.

Design and build PySpark applications to extract, transform, aggregate, and analyse large datasets.

Support migration of data assets to the cloud using GenAI-enabled automation approaches.

Work with large language models including pretraining, fine-tuning, augmentation, and optimisation use cases where applicable.

Identify, gather, and consolidate data from diverse sources including internal databases, spreadsheets, and enterprise systems.

Apply data quality checks, cleaning processes, and transformations using Python, Pandas, and SQL.

Automate repetitive data preparation and workflow tasks using scalable scripts and code-based practices.

Collaborate with stakeholders to clarify requirements, challenge assumptions, and validate data quality.

Document data workflows, coding standards, analytical methodologies, and best practices.

Proactively identify, communicate, and manage project risks, issues, dependencies, and delivery blockers.

Operate independently with minimal supervision while maintaining strong collaboration with business and technical teams.

Required Skills

15+ years of experience as a Data Architect, Data Engineer, Data Analyst, or related data-focused role.

Hands-on experience with Databricks on AWS.

Strong PySpark, Python, Pandas, SQL, and data analytics experience.

Experience with ETL tools such as Informatica.

Proven experience migrating data from on-premises platforms to AWS.

Experience designing and implementing scalable, secure, and cost-efficient data solutions on AWS.

Strong data modelling and database solution design experience.

Experience developing PySpark applications for extraction, transformation, aggregation, and insight generation.

Experience with GenAI-driven automation for cloud data migration or data modernization.

Exposure to LLM pretraining, fine-tuning, augmentation, and optimization.

Ability to automate data workflows and implement code-based best practices.

Strong documentation, communication, stakeholder engagement, and requirement clarification skills.

Preferred Skills

Experience with Python libraries such as Pandas, Scikit-learn, and Matplotlib.

Experience working with multiple structured and semi-structured data formats.

Strong understanding of data governance, data quality, and reproducibility practices.

Experience in large enterprise data modernization programs.

Bachelor's degree or higher in Computer Science, Data Engineering, Analytics, or a related field.

Behavioral Expectations

Excellent communication skills with the ability to explain analytical findings to technical and non-technical stakeholders.

Proactive approach to risk and issue management.

Strong critical thinking and problem-solving ability.

Ability to question assumptions, assess data quality, and recommend alternative approaches.

Self-directed, resourceful, and comfortable managing priorities independently.

Collaborative mindset with the ability to work closely with cross-functional teams.