Data Solution Architect

The Data Solution Architect is responsible for designing and delivering enterprise-grade data platforms and solutions that enable advanced analytics and business intelligence. This role requires deep expertise in data engineering, cloud-native architecture, and modern data technologies. The architect will work closely with engineering, product, and compliance teams to build secure, scalable, and regulatory-compliant data solutions. All solutions must align with the principles, standards, and reference architectures established by the Enterprise Architecture function, ensuring consistency, interoperability, and strategic fit across the organization.

Key Responsibilities

• Solution Architecture: Architect and deliver cloud-based data platforms (e.g., Azure Data Lake) and scalable data pipelines using technologies such as Databricks, Kafka, etc.
• ETL/ELT Frameworks: Develop and implement ETL/ELT frameworks for ingesting, transforming, and integrating data from diverse sources, including legacy financial systems, SaaS platforms, and real-time data streams.
• Data Platform Deployment: Design and oversee deployment of data warehouses, lakehouses, and analytics sandboxes to support business intelligence, machine learning, and reporting needs inclusive of Power BI and Snowflake.
• Data Governance: Define and enforce data governance, metadata management, and data quality standards in accordance with Enterprise Architecture guidance and regulatory requirements.
• Enterprise Architecture Collaboration: Collaborate with Enterprise Architecture to ensure all solutions adhere to architectural standards, reference models, and technology roadmaps.
• Technology Evaluation: Lead technical evaluations and proof-of-concept projects for emerging data technologies (e.g., real-time analytics, data mesh, AI/ML platforms).
• Technical Documentation: Produce deliverables such as architecture diagrams, data flow maps, security models, and technical documentation for solution handoff and operational support.
• Agile/DevOps Leadership: Guide Agile/DevOps teams in implementing CI/CD pipelines, automated testing, and infrastructure-as-code for data solutions.

Expected Skills

• Data engineering platforms, big data technologies, and cloud-native development (microservices, Kubernetes).
• API design (REST) and integration tools (ETL/ELT).
• Stakeholder communication (technical and non-technical audiences).
• Leadership in Agile/DevOps environments.
• Analytical problem-solving for complex data systems.
• Project management: proficiency in tools like Jira; familiarity with SDLC and CI/CD pipelines.

Expected Knowledge

• Foundational Understanding: Data modeling, data pipeline orchestration, data quality management, metadata management, and data lifecycle.
• Data Platforms: Data lakes, warehouses, and lake house architectures; distributed systems and cloud data services.
• Data Engineering: ETL/ELT processes, real-time and batch data processing, data integration, and automation.
• Financial Systems: Payment networks, liquidity management, and settlement processes (as relevant to data flows).
• Regulatory Landscape: Global data privacy regulations (GDPR, CCPA) and compliance tools.
• Security: Data encryption, access controls, and penetration testing methodologies.

Qualifications

• Education: Bachelor’s/Master’s in Computer Science, Engineering, or related field.
• Certifications: AWS/Azure Solutions Architect, Certified Data Engineer, or similar
• Experience: 5 years in solution architecture or technical implementation, with 2 years in data engineering or enterprise data platforms.
• Proficient in system design, integration, and implementation across multiple technology domains.
• Proven track record in deploying scalable financial systems (e.g., payment gateways, digital wallets, stablecoin or cryptocurrency systems preferred)
• Hands-on experience with cloud platforms (Azure/AWS/GCP), containerization (Kubernetes), and DevOps practices.
• Excellent communication, collaboration, and critical thinking skills.
• Understanding of financial regulations, compliance, and security standards.

Expected Deliverables

• End-to-end solution architectures for data platforms, including detailed diagrams, technology stack specifications, and integration patterns.
• ETL/ELT pipeline designs and implementation plans for onboarding new data sources and modernizing legacy systems.
• Data governance frameworks, including policies for data quality, lineage, and access control, aligned with Enterprise Architecture standards.
• Technical documentation packages: architecture blueprints, operational runbooks, and security protocols.
• Prototypes and proof-of-concept implementations demonstrating new data engineering capabilities (e.g., streaming analytics, automated data quality checks).
• Performance benchmarks and scalability assessments for deployed solutions.
• Risk mitigation plans, including security audit findings and disaster recovery strategies.

This role combines technical depth with strategic vision to build data platforms that bridge traditional finance and modern analytics ecosystems. The ideal candidate will balance innovation with operational rigor to deliver secure, compliant, and user-centric data solutions.