Data Engineering Lead At NBC November 2023

Data Engineering Lead At NBC November 2023

Data Engineering Lead

NBC is the oldest serving bank in Tanzania with over five decades of experience. We offer a range of retail, business, corporate and investment banking, wealth management products and services.

Job Summary

This role involves designing, developing, and maintaining data infrastructure and data pipelines to ensure that data is efficiently collected, processed, stored, and made accessible for analysis and decision-making within an organization. The role of a Data Engineering Lead is instrumental in supporting data-driven initiatives. This role is instrumental in shaping an organization’s data landscape. The ability to blend technical expertise with a strong understanding of business needs is essential in this role.

Job Description

Design, develop, and maintain data pipelines for extracting, transforming, and loading (ETL) data from various sources into data storage systems.
Create efficient and scalable ETL processes to ensure the smooth flow of data and its availability for analysis.
Integrate data from multiple sources, such as databases, APIs, third-party services, and logs, into a unified data ecosystem.
Implement data quality checks and validation processes to ensure data accuracy and consistency.
Develop and maintain data models, schemas, and database structures to optimize data storage and retrieval.
Design and maintain data warehouses, data lakes, or other storage solutions for data analytics.
Utilize big data technologies such as Hadoop, Spark, Kafka, and NoSQL databases for processing and analysing large volumes of data.
Stay up to date with emerging data technologies and evaluate their applicability to the organization’s data infrastructure.
Create and maintain data documentation, data dictionaries, and data lineage to support data governance and audit requirements.
Implement data governance best practices and adhere to data security and compliance standards.
Optimize database performance, including query tuning, indexing, and data partitioning, to enhance system performance and reduce latency.
Implement data compression, archiving, and data retention policies to manage storage costs.
Implement data security measures, encryption, and access controls to protect sensitive data and ensure compliance with data protection regulations.
Collaborate with data scientists, analysts, and other stakeholders to understand their data needs and provide the necessary infrastructure for data-driven decision-making.
Work with cross-functional teams to define data requirements for new projects and initiatives.
Set up monitoring and alerting systems to proactively identify and resolve data pipeline issues, data quality problems, or performance bottlenecks.
Conduct root cause analysis and troubleshooting of data-related problems.
Ensure that data infrastructure is scalable to accommodate growing data volumes and evolving business requirements.
Continuously optimize data pipelines and infrastructure for improved performance and cost-efficiency.
Implement automation for deployment, data pipeline scheduling, and routine maintenance tasks to increase operational efficiency.
Keep the team and relevant stakeholders informed about data engineering best practices and new technologies.
Provide training and knowledge sharing sessions to help others understand and utilize data systems effectively.