Data Engineer

Job Description

The Data Engineer will be responsible for expanding and optimizing the data and
data pipeline architecture, as well as optimizing data flow and collection for cross
functional teams. The Data Engineer is an experienced data pipeline builder and
data wrangler who enjoys optimizing data systems and building them from the
ground up. The Data Engineer will support software developers, database
architects, data analysts and data scientists on data initiatives and will ensure
optimal data delivery architecture is consistent throughout ongoing projects. They
must be self-directed and comfortable supporting the data needs of multiple teams,
systems and products. The Data Engineer will be required to optimise or even re-
design the data architecture to support the next generation of products and data
initiatives. The Data Engineer is also responsible for the maintenance,
improvement, cleaning, and manipulation of data in the business’s operational and
analytics databases in order to understand and aid in the implementation of
database requirements, analyse performance, and troubleshoot any existent
issues.

The Data Engineer has to be an expert in SQL development further providing
support to the Data and Analytics in database design, data flow and analysis
activities. The position of the Data Engineer also plays a key role in the
development and deployment of innovative big data platforms for advanced
analytics and data processing.

• Key responsibilities for the Data Engineer include (but are not limited to):
• Create and maintain optimal data pipeline architecture, assemble large, complex data sets that meet functional and non-functional business requirements.
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability.
• Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and ‘big data’ technologies.
• Implement analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics.
• Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
• Keep our data separated and secure across national boundaries through multiple data centres.
• Implement data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.
• Work with data and analytics experts to strive for greater functionality in our data systems.
• Supports junior data engineering personnel by creating databases optimized for performance, implementing schema changes, and maintaining data architecture standards across all of the business’s databases.
• The Data Engineer leads innovation through exploration, benchmarking, making recommendations, and implementing big data technologies for platforms.
• Develop and implement scripts for database maintenance, monitoring, performance tuning, and so forth.
• Designing and developing scalable ETL packages (Abinitio) from the business source systems and the development of ETL routines in order to populate databases from sources and also to create aggregates.
• Oversee large-scale data Hadoop platforms and support the fast-growing data within the business.
• The Data Engineer is responsible for enabling and running data migrations across different databases and different servers, for example, data migration from SQL servers to Oracle.
• Define and implement data stores based on system requirements and other requirements.
• Perform thorough testing and validation in order to support the accuracy of data transformations and data verification used in machine learning models. Ensure proper data governance and quality across the Data and Analytics department and the business as a whole.
• The Data Engineer plays an analytical role where ad-hoc analyses of data stored in the business’s databases is performed and writes SQL scripts, stored procedures, functions, and views.
• Troubleshoot data issues within the business and across the business and presents solutions to these issues.
• Proactively analyse and evaluate the business’s databases in order to identify and recommend improvements and optimization.
• Analyse complex data elements and systems, data flow, dependencies, and relationships in order to contribute to conceptual physical and logical data models.
• Develop and implement scripts for database maintenance, monitoring, and performance tuning to be applied across the business.

• The Data Engineer also plays a supporting role to the data warehousing department in the implementation of the data warehouse for the new big data platforms. He/She works collaboratively with the entire Data and Analytics team, providing support to the entire department for its data centric needs.
• Keep up with industry trends and best practices, advising management on new and improved data engineering strategies that will drive departmental performance leading to improvement in overall data governance across the business, and promoting informed decision-making, ultimately improving overall business performance.

Technical skills:

• Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
• Experience building and optimizing ‘big data’ data pipelines, architectures and data sets.
• Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
• Strong analytic skills related to working with unstructured datasets.
• Build processes supporting data transformation, data structures, metadata, dependency and workload management.
• A successful history of manipulating, processing and extracting value from large, disconnected datasets.
• Working knowledge of message queuing, stream processing, and highly scalable ‘big data’ data stores.
• Strong project management and organizational skills.
• Experience supporting and working with cross-functional teams in a dynamic environment.

Experience and Related Skills

• We are looking for a candidate with 5+ years of experience in a Data Engineer role, who has attained a Graduate degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field. They should also have experience using the following software/tools:
• Experience with big data tools: Hadoop, Spark, Kafka, etc. Experience with relational SQL and NoSQL databases, including Postgres and Cassandra.
• Experience with data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc.
• Experience with AWS cloud services: EC2, EMR, RDS, Redshift, Abinitio
• Experience with stream-processing systems: Storm, Spark-Streaming,
  etc.
• Experience with object-oriented/object function scripting languages:
  Python, Java, C++, Scala, etc.
• The candidate must demonstrate experience working with large and complex data sets as well as experience analyzing volumes of data through basic Microsoft Excel functions, for example, macros and pivot tables. A candidate for this position will also have had experience working in internet technologies, for example, SaaS, IaaS, and PaaS.
• A suitable candidate will also have had experience in the creation and debugging of databases critical to the business’s mission. The candidate will have strong working and conceptual knowledge of building and maintaining physical and logical data models and experience with Tableau, Domo or other business intelligence tools.
• A suitable candidate for the position will also have had system management expertise with monitoring, disaster recovery, backup, automated testing, automated schema migration, and continuous deployment.
• Communication Skills: Communication skills for the Data Engineer are non- negotiable. Communication skills will be needed in his role where he has to convey messages and instructions clearly to the supporting personnel in order to ensure efficient execution of duties within the junior department.
• Technological Savvy/Analytical Skills: The Data Engineer must also have exceptional analytical skills, showing fluency in the use of tools such as MySQL and strong Python, Shell, Java, PHP, and T-SQL programming skills. He/She must also be technologically adept, demonstrating strong computer skills.
• The candidate must additionally be capable of developing databases using SSIS packages, T-SQL, MSSQL, and MySQL scripts.
• The candidate will also have an ability to design, build, and maintain the business’s ETL pipeline and data warehouse. The candidate will also demonstrate expertise in data modelling and query performance tuning on SQL Server, MySQL, Abinitio ,Redshift, Postgres or similar platforms.
• Interpersonal Skills: A candidate for the position must also possess certain personal attributes that make him more suited for the position. The candidate must be result-driven, be an analytical and creative thinker, be an innovative problem solver, be self-motivated and proactive, be highly organized, have
  ability to handle-multiple and simultaneous tasks meeting aggressive
  deadlines, be a team player, and demonstrate an exceptional ability to stay
  calm and composed in the face of adversity.

Business Skills:

• · Depth in data modelling and database design: This is the core skill of the data architect. The effective data architect is sound across all phases of data modelling, from conceptualization to database optimization. In his experience this skill extends to SQL development and perhaps database administration.
• Breadth in established and emerging data technologies: In addition to depth in established data management and reporting technologies, the data architect is either experienced or conversant in emerging tools like columnar and NoSQL databases, predictive analytics, data visualization, and unstructured data. While not necessarily deep in all of these technologies, the data architect is experienced in one or more and must understand them sufficiently to guide their area and the organization in understanding and adopting them.
• Ability to conceive and portray the bigger data picture: When the data architect initiates, evaluates, and influences projects they do so from the perspective of the entire organization and with focus on their area. The data architect maps the systems and interfaces used to manage data, sets standards for data management, analyses current state and conceives desired future state, and conceives projects needed to close the gap between current state and future goals.
• Analytical Problem-Solving: Approaching high-level data challenges with a clear eye on what is important; employing the right approach/methods to make the maximum use of time and human resources.
• · Creative Problem-Solving: Approaching data organization challenges with a clear eye on what is important; employing the right approach/methods to make the maximum use of time and human resources.
• Industry Knowledge: Understanding the way our banking industry functions and how data can be collected, analyzed and utilized; maintaining flexibility in the face of large data developments.

Qualifications:

• The Data Engineer must have a bachelor’s degree in computer science, Applied Mathematics, Engineering, or any other technology related field. An equivalent of the same in working experience is also accepted for the position.
• 5 years of experience in managing financial services data, especially as a data architect
• Advanced data analysis skills using Excel, Abinitio,SQL and reporting tools to import, analyse and report on data
• Relative certifications

 

Important Closing Date Note

Take note that applications will not be accepted on the below date and onwards, kindly submit applications ahead of the closing date indicated below.

11/05/26