Job Description:
As the Head of Data Engineering & Data Analytics, your primary responsibility is to oversee
the design, development, and maintenance of the infrastructure necessary for the collection,
storage, and processing of large volumes of data. You will play a pivotal role in supporting
business functions like growth, marketing, product design, underwriting, collections, claim
settlement, and more by making informed data-driven decisions.
Responsibilities:
- Define and build scalable data pipelines, design data models, and ensure data integration
from different systems and sources. - Implement data quality checks, monitoring, and governance processes to maintain data
integrity and regulatory compliance. - Continually improve data processing and storage performance to ensure efficient data
retrieval and analysis. - Develop and maintain automated data workflows and processes to streamline data
operations and reduce manual intervention. - Manage the data infrastructure, including databases, cloud-based services, and storage
systems. - Collaborate closely with cross-functional teams to understand their data needs and provide
data engineering support for various projects and initiatives. - Identify and resolve data-related issues, ensuring data availability and reliability for
end-users. - Define metrics for business processes and develop metric dashboards to track success
and performance metrics. - Stay up-to-date with industry trends and best practices, adopting new technologies and
methodologies that enhance data engineering processes.
Requirements: - Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- Proficiency in programming languages like Python, Java, or Scala.
- Familiarity with big data technologies (e.g., Hadoop, Spark) and cloud-based data
platforms (e.g., AWS, Azure, GCP). - Experience with data modelling, ETL processes, and data integration techniques.
- Strong understanding of database systems (e.g., SQL, NoSQL) and data warehousing
developments. - Problem-solving skills and the ability to optimise data processes for performance and
efficiency. - Excellent communication and teamwork abilities to collaborate effectively with
cross-functional teams. - Hands-on experience with SQL and any BI platform (Tableau, PowerBI, Qlikview, Looker,
Quicksight, etc). - Conceptual understanding of basic statistical concepts (Sampling, Distributions, Central
tendency, Hypothesis testing, etc). - Ability to identify relevant data to solve business problems and (in)validate hypotheses.
- (Good to have) Prior data modelling experience in R/Python and some classification and/or
regression techniques.