Boost Bank operates in the fast-growing digital banking and micro-lending ecosystem, focused on enabling financial inclusion for SMEs and underserved segments. Data is a core strategic asset powering risk decisions, regulatory compliance, and customer experience.
We are seeking a Lead Data Architect to lead, design, and continuously improve Boost Bank's enterprise data platforms. This role requires deep hands‑on expertise in data engineering, data modelling, and data architecture, combined with strong leadership and a high level of curiosity to optimise data engineering processes using AI, automation, and emerging technologies.
The role requires both strategic vision and hands-on technical depth in data engineering, advanced data modelling, and enterprise architecture, combined with leadership responsibilities. The candidate is expected to design, build, and review production-grade data pipelines and models, while guiding the broader team and architecture direction.
- Act as a hands‑on technical leader, actively contributing to data engineering, data modelling, and architecture design.
- Lead Data Engineering delivery, including hands-on development, code reviews, and troubleshooting of complex data pipelines.
- Architect and implement enterprise data platforms and data products that support analytics, regulatory reporting, and business use cases.
- Act as the technical authority for data modelling (conceptual, logical, physical), ensuring scalable and reusable data structures.
- Administer and govern cloud data platforms, including security, access control, monitoring, and cost optimization.
- Define and enforce data governance, standards, and architecture principles, ensuring they are implemented in code and pipelines, not just documented.
- Architect data solutions that leverage AI, metadata, automation, and self‑service capabilities to improve data availability and reliability.
- Mentor and coach Data Engineers to adopt AI‑assisted development, automated testing, and data quality frameworks.
- Drive innovation in data architecture, engineering tooling, and platform operations.
Need to Know (Knowledge & Skills)
- Strong hands‑on expertise in data engineering, including batch and streaming pipeline development.
- Advanced knowledge of data architecture and data modelling (logical, physical, and conceptual).
- Extensive experience with AWS ecosystem (S3, Glue, Athena, Redshift, EMR, CloudFormation. Lake formation) and other modern cloud data platforms such as Databricks, experience working across multi‑cloud environments will be an added advantage.
- Strong proficiency in SQL (advanced querying and optimization). Python and scripting (Bash), Advantage: Scala
- Deep understanding of big data and analytics technologies such as Apache Spark, Kafka, Hadoop ecosystem
- Experience with relational and NoSQL databases, PostgreSQL, MySQL, MongoDB.
- Strong knowledge of banking and financial data domains, including supporting regulatory reporting data pipelines in banking/fintech environments
- Experience implementing data governance, data quality, lineage, and security controls.
- Experience applying automation and AI techniques to Data validation and anomaly detection, Metadata and lineage automation, Improving developer productivity and pipeline reliability
- Familiarity with FinOps principles and cloud cost optimization.
- Infrastructure & Engineering Practices with CI/CD for data pipelines, DevOps and version control practices, Linux/Unix environments.
Need to Be (Attributes & Competencies)
- Strong hands-on builder mindset actively contributes to implementation, not just design.
- Highly curious and continuously seeking ways to optimize data engineering processes.
- High ownership and accountability for end-to-end data platform delivery.
- Ability to think in systems and trade-offs (cost vs performance vs scalability vs security).
- Strong focus on data quality, reliability, and governance.
- Excellent problem-solving and analytical thinking capability.
- Strong communication skills with ability to translate business needs into technical solutions.
- Able to balance delivery speed, quality, security, scalability, and regulatory compliance.
- Comfortable operating in a fast‑paced, regulated, digital banking environment.
Experience
- 8–12 years of overall experience in data engineering, data architecture, or analytics platform roles.
- 3+ years of experience in a senior or lead capacity, with accountability for architecture decisions and technical delivery.
- Demonstrated experience leading data engineering teams or acting as the primary technical authority for data platforms.
- Prior experience supporting banking, financial services, or regulated environments is strongly preferred.
- Proven track record of working with cloud‑based data platforms (Databricks, AWS / Azure / GCP), including production‑grade systems.
Education
- Bachelor's or Master's degree in Computer Science, IT, Engineering, or a related technical field.
- Relevant certifications in cloud platforms, data engineering, or data architecture are an advantage
