DATA ENGINEER

SUMMARY:
Individual must be ready to support financial services data platform in a multi-tenant environment.

POSITION INFO:
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.