CRDB BANK CAREER OPPORTUNITIES – JUNE 2026
CRDB Bank is seeking qualified, dynamic, and experienced professionals to fill the following exciting positions within our Department of Retail Banking at the Tanzania Head Office. If you are passionate about driving innovation, customer insights, and digital transformation in the banking sector, we invite you to explore these opportunities.
1. MANAGER MARKET RESEARCH AND INSIGHT
Department: Department of Retail Banking
Location: Tanzania Head Office
Number of Openings: 1
Employment Terms: PERMANENT
Job Purpose
The Manager - Market Research & Insights is responsible for leading the Bank's market and customer research initiatives to identify growth opportunities, understand customer behaviour, and assess competitive and market dynamics. The role serves as the “voice of the customer” and emerging trends identifier, with a strong ability to translate complex qualitative and quantitative data into actionable insights that drive product innovation, digital ecosystem development, and enhanced customer experience.
Principle Responsibilities
- Lead research initiatives to conduct gap analysis and identify areas for improvement across both existing and newly launched products, services, and features.
- Assess customer adoption, satisfaction levels, and value realization to inform product and service optimization.
- Monitor, analyse, and forecast industry, consumer, and digital banking trends, including emerging fintech developments and regulatory changes.
- Identify market opportunities, threats, and unmet customer needs to inform business, digital, and innovation strategies.
- Track market dynamics and emerging competitive threats to support strategic decision-making.
- Develop customer personas, behavioural segments, and end-to-end journey maps using demographic, behavioural, and socioeconomic data.
- Generate deep customer insights to guide lifecycle marketing, product development, service design, and customer experience initiatives.
- Conduct regular competitor benchmarking on pricing, service standards, customer value propositions, and digital platform capabilities.
- Analyse competitor movements and offerings to inform positioning and differentiation strategies.
- Design and execute research experiments and studies with rigor, ensuring data accuracy, integrity, and reliability.
- Select and apply appropriate research methodologies based on study objectives, including focus groups, mystery shopping, surveys, and Net Promoter Score (NPS) studies.
- Analyse and interpret complex research and experimental datasets to identify patterns, validate hypotheses, and derive actionable insights.
- Translate analytical outcomes into clear business implications, recommendations, and decision support materials.
- Present research findings, insights, and project progress at internal stage-gate reviews and to cross-functional stakeholders, clearly articulating risks, opportunities, proposed solutions, and commercial impact.
- Ensure all research activities comply with internal brand standards, ethical research practices, and data privacy and protection regulations.
- Maintain strong governance over research processes, methodologies, and outputs to ensure the credibility, consistency, and reliability of insights.
- Lead talent identification, mentoring, and skills development while ensuring SMART KPIs are clearly defined, issued, and consistently monitored for all unit staff.
- Drive performance reviews and effective feedback processes alongside fostering strong staff engagement and team motivation within the unit.
- Maintain strong relationships with business partners while leading contract negotiations and management to ensure mutual benefits, supported by solid research expertise and experience.
Qualifications Required
- Bachelor's degree in Banking, Finance, Statistics, Marketing, Economics, or a related field.
- MBA or Master's degree in a relevant discipline is highly preferred.
- Minimum of 5 years' experience in market research, customer insights, or analytics roles.
- Experience within a financial institution, digital business, or specialized research agency is strongly preferred.
- Strong ability to produce accurate and reliable market forecasts and insight-driven recommendations.
- Advanced research, analytical, and problem-solving skills.
- Expertise in statistical and analytical tools such as SPSS, SAS, R, and/or similar platforms.
- Proficiency in data visualization tools such as Tableau or Power BI.
- Financial Acumen: Strong understanding of banking operations, financial products, market structures, and industry trends.
- Communication & Storytelling: Excellent ability to translate complex data into compelling, executive-ready insights and narratives.
- Apply strategic thinking and innovation portfolio management to drive forward-looking initiatives and prioritize impactful opportunities.
- Engage and influence stakeholders effectively while making sound decisions under uncertainty.
2. SENIOR DATA ENGINEER
Department: Department of Retail Banking
Location: Tanzania Head Office
Number of Openings: 1
Employment Terms: PERMANENT
Job Purpose
The Senior Data Engineer is responsible for designing, building, and maintaining the scalable data pipelines and ingestion frameworks that power the Digital Banking department. This role focuses on translating disparate, high-volume raw data streams—from mobile apps, internet banking portals, and payment gateways—into structured, clean, and highly optimized data stores. The Senior Data Engineer ensures that data is consistently available, accurate, and structured to support real-time reporting, advanced business intelligence, and production-ready machine learning models.
Principle Responsibilities
- Design, implement, and optimize scalable batch and real-time data ingestion pipelines using distributed computing frameworks like PySpark.
- Build and maintain resilient data lakes and warehousing environments, managing storage formats (e.g., Parquet, Delta) and metadata cataloging systems such as a Hive Metastore backed by PostgreSQL or object storage.
- Structure and partition large datasets to ensure low-latency query performance for downstream consumers (BI Analysts and Data Scientists).
- Implement strict data contract definitions, schema registries, and quality validation checks within pipelines to catch upstream system changes before they break downstream models or reports.
- Ensure data pipelines adhere to strict banking data privacy regulations, masking sensitive customer details, managing access control levels, and archiving historical logs securely.
- Maintain a highly transparent, clear data catalog mapping out data lineage from core systems to final analytics tables.
- Work to deploy data pipelines via containerized environments (Docker/Kubernetes).
- Serve as the primary technical point of contact for the BI Team and Data Science Team, translating their business analytics requirements into optimized backend data assets.
- Enforce clean, modular, and optimized SQL and Python coding standards for data engineering, ensuring thorough version control (Git) and documentation.
Qualifications Required
- Bachelor's degree in Computer Science, Software Engineering, Information Systems, Data Science, Statistics, Mathematics, or any related field.
- Minimum of 3 years of professional experience as a Data Engineer or Core Database Developer, with a proven track record of managing production-grade data pipelines.
- Advanced, hands-on experience using PySpark/Spark to extract, transform, and load massive, complex datasets.
- Deep understanding of managing decoupled data environments, file storage types (Parquet etc), and metadata catalogs (Hive Metastore).
- Expert-level proficiency in writing and optimizing complex queries, indexing, and modeling data structures within relational engines (e.g., PostgreSQL, Oracle).
- Strong familiarity with container tools (Docker) and modern data orchestration workflows (e.g., Apache Airflow or cron-based job scheduling).
- Dedication to automation, building resilient architectures that can recover from network timeouts, API failures, or source data spikes without manual intervention.
- An obsessive eye for identifying performance bottlenecks in queries and pipeline steps to minimize computing costs and execution time.
- Excellent technical communication skills, allowing for seamless collaboration with data consumers to understand exactly how the data needs to be shaped.
- Flexible and adoptive to market dynamics and experimentation.
- Customer-centric mindset.
- Self-driven and problem-solving skills.
3. SENIOR DIGITAL DATA SCIENTIST
Department: Department of Retail Banking
Location: Tanzania Head Office
Number of Openings: 1
Employment Terms: PERMANENT
Job Purpose
The Senior Data Scientist is the principal technical architect and hands-on lead for the data science team. This role is responsible for designing, building, and deploying highly scalable machine learning models that optimize digital banking products, predict customer behavior, and manage risk. The Senior Data Scientist bridges the gap between pure research and production engineering, ensuring the team's code is robust, automated, and seamlessly integrated into live banking workflows.
Principle Responsibilities
- Design, train, and validate complex predictive and prescriptive models (e.g., real-time credit scoring algorithms, predictive churn models, and recommendation engines).
- Build scalable, repeatable feature engineering pipelines that process large volumes of digital banking log data, utilizing tools like PySpark and SQL.
- Package and containerize models (using Docker) and work with system architects to deploy them as low-latency microservices/APIs into production.
- Establish and enforce best practices for version control (Git), code modularity, documentation, and automated testing across the data science pipeline.
- Peer-review and audit the mathematical frameworks and assumptions made in models built by junior data scientists to prevent overfitting, bias, or data leakage.
- Actively mentor Junior Data Scientists, guiding them through complex algorithmic challenges, feature selection techniques, and hyperparameter tuning.
- Stay at the forefront of AI/ML trends, assessing how new techniques (e.g., Large Language Models, advanced graph analytics for fraud detection) can be applied to digital banking.
Qualifications Required
- Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field.
- Minimum of 4 years of professional experience as a Data Scientist, with a documented history of shipping machine learning models directly into production environments.
- Expert-level Python skills, with fluency in the data science stack (Scikit-Learn, Pandas, NumPy).
- Practical experience processing high-velocity transaction data using distributed computing frameworks, specifically PySpark/Spark.
- Strong proficiency with containerization (Docker) and setting up persistent metadata environments (e.g., interacting with a Hive Metastore backed by relational or object storage).
- Advanced SQL skills optimized for writing complex queries over large data stores (e.g., PostgreSQL, Oracle).
- The ability to look at a complex banking problem and architect an algorithmic solution.
- Ability to manage high-performing technical minds, fostering a collaborative, non-siloed engineering culture.
- Flexible and adoptive to market dynamics and experimentation.
- Customer-centric mindset.
- Self-driven and problem-solving skills.
4. MLOPS ENGINEERING MANAGER
Department: Department of Retail Banking
Location: Tanzania Head Office
Number of Openings: 1
Employment Terms: PERMANENT
Job Purpose
The MLOps Engineering Manager is an engineering role responsible for the infrastructure, automation, and pipelines that power both Data Engineering and Machine Learning operations. This role leads the design, deployment, and maintenance of scalable data platforms (e.g., Hive Metastores, PostgreSQL, MinIO/S3 architectures) and robust MLOps lifecycles. The Manager ensures that raw digital banking data flows seamlessly to the BI/Data Science teams, and that predictive models are deployed into production via automated, low-latency, and secure CI/CD pipelines.
Principle Responsibilities
Infrastructure Architecture & Data Engineering Leadership
- Oversee the architecture and optimization of the department's persistent data platforms, including distributed object storage, Hive Metastores, and relational databases.
- Work with the Senior Data Engineer to ensure that robust, containerized data pipelines (e.g., PySpark workflows running on Docker/Kubernetes) are optimized for low-latency transaction processing.
- Manage cluster resources, storage scaling, and compute environments to balance performance with infrastructure costs.
MLOps Strategy & Automated CI/CD Lifecycles
- Design and enforce automated CI/CD pipelines for deploying Machine Learning models as highly available, production-grade microservices/APIs.
- Implement automated infrastructure to track model telemetry, monitoring API latency, prediction accuracy, and data/concept drift in real-time.
- Own the orchestration and containerization strategy (Docker, Kubernetes) to guarantee that environments match perfectly from a data scientist's local sandbox to the production cluster.
- Act as the critical operational bridge linking the Data Science Manager (who designs the models) and the BI Manager (who consumes the data assets) with the bank's core Core Banking IT and Security teams.
Qualifications Required
- Bachelor's in Computer Science, Information Technology, Software Engineering, Data Engineering, or a related technical field.
- Minimum of 5 years of experience spanning DevOps, Cloud/Infrastructure Engineering, Data Engineering, or MLOps with at least 1–2 years in a lead or managerial position.
- Proficiency with Docker and Kubernetes for scaling data and model workloads.
- Strong hands-on experience setting up, tuning, and decoupling data architectures using Hive Metastores, PostgreSQL, and high-performance object storage.
- Deep familiarity with modern deployment and tracking pipelines (e.g., Git Actions or cloud-native equivalents etc.).
- Strong mastery of Python, shell scripting, and distributed processing environments using PySpark.
- The ability to design modular, decoupled data architectures that prevent systemic bottlenecks.
- A strong “infrastructure as code” and automation-first mindset; a refusal to accept manual, fragile deployment steps.
- Ability to negotiate infrastructure access, security clearances, and server resource allocations with centralized corporate IT divisions.
- Flexible and adaptive to market dynamics and experimentation.
- Customer-centric mindset.
- Self-driven and problem-solving skills.
APPLICATION INSTRUCTIONS
Deadline for all applications: June 30, 2026
All positions are permanent and based at the CRDB Bank Tanzania Head Office.
Qualified candidates are invited to submit their applications by clicking the link below:
CRDB Bank is an equal opportunity employer. Only shortlisted candidates will be contacted.