DATA ENGINEER

An established insurance company is seeking to hire a highly skilled and experienced Data Engineer to join their team. Your:

Formal Education:

• Degree in Data Science, Information Technology, Computer Science or equivalent

Advantageous :

• Cloud Data Certifications
• Exposure to regulated environments,financial services, fintech

Experience:

• Minimum of 2 years in a data engineer role or a similar technical role.

Responsibilities:

• Build and maintain ETL pipelines supporting a multi-tenant data platform, ingesting data from APIs, databases, and event sources.
• Build and maintain Data Platform APIs that allow teams to ingest, process, and access data easily and reliably.
• Implemented tenant-specific logic by following existing configuration and naming conventions.
• Apply tenant-level data isolation using schemas, partitions, or access controls
• Build models from existing templates used for financial and operational reporting. Develop models for analytics and reporting, maintaining consistency with shared data models.
• Monitor scheduled pipelines, investigate failures, and resolve data quality issues and inconsistencies.
• Maintain daily and incremental data loads into the data warehouse.
• Assist with onboarding new clients by validating source data and testing pipeline outputs.
• Work closely with senior data engineers to learn patterns for multi-tenant data isolation.
• Collaborate with analytics, product, and customer facing teams to understand reporting needs.
• Support strict regulatory and audit requirements by following data handling,retention, and audit guidelines.
• Handle financial and sensitive data (PII) according to company policies and regulatory standards (e.g. POPIA)
• Apply least-privilege access and rolebased access controls, and support data protection through masking, encryption,and established security standards.

Competencies & Skills:

Technical Skills:

• Programming languages – Good knowledge of programming languages such as Python, especially used for pipeline and data manipulation.
• SQL – working experience using SQL for data cleaning, aggregation, data transformation and integration.
• Data Warehouse - have a fundamental understanding of data warehousing solutions and platforms.
• Databases – hands on experience working with relational and non-relational databases.
• Cloud computing – comfortable building data solutions using cloud hosted services or data platforms.

Data Engineering Practice:

• Analytics skills – strong problem solving skills, understand data characteristics, identify patterns, and data quality issues.
• Data modelling and ETL – Is able to communicate and translate business requirements into existing data models.
• Data Pipeline Development– build and validate smaller scale data pipelines independently.
• CI/CD and Version Control – apply best practice for managing pipelines and data workflows.