Data Solution Architect

We are seeking a Data Solution Architect to design and implement scalable, cloud-native data solutions across both OLAP (analytical) and OLTP (operational) architectures, applying advanced data management and governance practices within a large-scale banking environment. This role provides technical leadership on modern data platforms — spanning Data Lakes, Operational Data Stores, real-time streaming, and Data Mesh — ensuring solutions are secure, performant, cost-optimised, and aligned with enterprise data strategy. The successful candidate will combine deep data engineering expertise with architectural vision, working across business, data engineering, and analytics teams to deliver data solutions that support both operational excellence and advanced analytics use cases.

Responsibilities

Design Data Lake and Operational Data Store architectures using cloud-native technologies to support both analytical (OLAP) and operational (OLTP) workloads, ensuring proper separation, scalability, and performance.

Define and enforce Data Mesh principles for decentralized data ownership and governance, establishing domain-oriented data products, self-serve data infrastructure patterns, and federated governance.

Develop conceptual, logical, and physical data models for analytical and operational use cases, ensuring models are well-documented, fit for purpose, and aligned with enterprise data standards.

Design batch and real-time data ingestion pipelines using Apache Kafka and other streaming technologies, ensuring reliable, low-latency data flow from source systems to Data Lakes, ODS, and downstream consumers.

Build and manage scalable ETL/ELT processes for data cleansing, validation, transformation, and enrichment across multiple source systems including core banking, payments, and transaction banking platforms.

Define patterns for data pipeline orchestration, error handling, retry logic, and monitoring to ensure production-grade reliability and observability.

Utilize AWS services such as S3, Glue, Redshift, Lambda, Kinesis, Airflow, and SageMaker to deliver end-to-end cloud-native data solutions aligned with security, cost optimization, and scalability principles.

Support advanced analytics and machine learning use cases by designing data pipelines and platform capabilities that provide governed, high-quality data to data science and business intelligence teams.

Implement data quality frameworks incorporating validation, profiling, and anomaly detection to ensure data trustworthiness.

Establish and maintain metadata management and data lineage tracking to provide transparency into data origins, transformations, and usage.

Define data standards, policies, and best practices collaboratively with data owners, compliance, and business stakeholders to align with regulatory and governance frameworks.

Collaborate with data product owners, data engineers, analytics teams, and business stakeholders to understand requirements, define data products, and deliver solutions driving measurable business value.

Skills

Must have

The role requires extensive expertise in data architecture and engineering within large-scale enterprise settings, focusing on designing and implementing complex data platforms. The candidate should demonstrate practical knowledge of data lake, Operational Data Store (ODS), and Data Mesh architectures, employing these frameworks to develop robust production environments. Proficiency in crafting and optimizing data pipelines is essential, including batch and real-time processes for ingestion, cleansing, validation, transformation, and modeling.

Hard skills include a deep understanding of real-time streaming technologies, particularly Apache Kafka, covering topic design, partitioning, consumer group management, and integration with downstream systems. The candidate must be adept at leveraging AWS data services such as S3, Glue, Airflow, Redshift, Lambda, Kinesis, SageMaker (including SageMaker Unified Studio), and Amazon QuickSight to architect comprehensive cloud-native data solutions. Strong capabilities in data modeling across conceptual, logical, and physical layers are required, with experience in dimensional and normalized schemas suited for both analytical and operational workloads.

Soft skills involve advanced problem-solving and analytical abilities to identify and resolve complex data quality issues, pipeline failures, and performance challenges in distributed systems. Effective communication and stakeholder management are critical, enabling collaboration with technical teams, data product owners, business leaders, and governance stakeholders to ensure alignment and successful delivery. Experience within the banking domain, specifically transaction banking in areas such as trade finance, payments, cash management, and supply chain finance, is preferred.

Hard skills:

Expertise in data architecture frameworks and production implementations.

Proficient in real-time data streaming with Apache Kafka.

Skilled in AWS data service platforms for end-to-end cloud-native solutions.

Strong data modeling across various schema designs and workloads.

Soft skills:

Analytical problem-solving for complex distributed data systems.

Effective communication and stakeholder engagement.

Domain knowledge in commercial banking transaction services.

Nice to have

n/a

Other

Languages

English: C1 Advanced

Seniority

Lead