Data Engineer

Job Purpose

• The Data Engineer will design, develop, and maintain robust, scalable, and secure data pipelines and infrastructure that support data-driven decision-making across the bank.
• The Data Engineer will ensure efficient data ingestion, transformation, and storage from multiple banking systems, enabling timely access to accurate data for analytics, regulatory compliance, and operational reporting.

Strategic
Key Responsibilities:

• Enable Data-Driven Decision Making - Ensure reliable, scalable infrastructure to support analytics, machine learning, and business intelligence across the company.
• Modernize Data Infrastructure - Transition to scalable, and real-time platforms.
• Ensure Data Governance and Security - Design systems that comply with data privacy laws and internal governance policies.
• Support Scalability and Future Growth - Build systems that support exponential growth in data volume, variety, and velocity.

Initiatives

• Data Architecture - Build and maintain data models, data lakes, and data warehouses tailored to banking use cases.
• Implement ETL/ELT Pipelines - Design, develop, and optimize robust ETL/ELT pipelines to ingest data from internal banking systems (core banking, CRM, transactions, etc.) and external source.
• Establish Data Quality Frameworks - Introduce data validation, lineage, and anomaly detection to improve trust in data.

Operational

• Maintain and Monitor Data Pipelines - Ensure pipelines are running reliably, on schedule, and are monitored for failures.
• Optimize Query and Pipeline Performance - Tune SQL queries, job runtimes, and storage formats to reduce costs and latency.
• Handle Data Issues and Incidents - Respond to pipeline failures, data discrepancies, and outages swiftly.
• Document Data Architecture and Flows - Maintain updated documentation to assist new engineers, analysts, and stakeholders.
• Collaborate with Stakeholders - Work closely with data analysts, data scientists, product teams, and DevOps for cross-functional initiatives.

Key Responsibilities

• Data Pipeline Development - Design, build, and maintain ETL pipelines for ingesting and processing data and automate data workflows using orchestration tools.
• Data Architecture & Modeling - Design scalable data architectures and define and enforce data standards and naming conventions.
• Data Integration - Integrate data from multiple sources and build connectors to internal and external systems.
• Security & Compliance - Ensure data security, encryption and compliance with data governance and regulations.
• Support for Analytics Prepare and expose data to analysts and BI tools. Optimize queries and storage formats for analytical performance and build data marts and materialized views tailored for business reporting.
• Monitoring & Optimization - Monitor pipeline performance, latency, and failures.
• Troubleshoot and resolve data flow issues quickly.

Academic Qualifications

• BS/BA Degree preferably Computer Science, Information Systems or related field.
• Professional Qualifications / Membership to professional bodies/ Publication.
• (Desirable) AWS Certified Data Analytics.
• (Desirable) Azure Data Engineer Associate.

Work Experience Required

• 5+ years of experience as a data engineer or in a similar role, preferably in financial services or banking.
• Strong experience on database development and data model design.

Competencies

• Strong in Python and SQL (core languages for data manipulation).
• Familiarity with data warehousing solutions (star/snowflake schemas, normalization).
• Hands-on with modern data warehouses.
• Experience with both relational and NoSQL databases.
• Familiarity with big data tools and platforms.
• Strong understanding of data security, compliance, and banking regulations
• Strong analytical and problem-solving skills.
• Solid business and collaboration skills, and responsive to service needs and operational demands.
• Attitude to thrive in a fun, fast-paced environment.