Data Engineer-Unified data platform
Description
Demonstrate a sound understanding of:
Ingestion and transformation of data from multiple source
domains (e.g. CRM feeds, third-party data, digital signals) into the platform
following approved design principles and data governance standards.
Building and maintaining batch and streaming transform
pipelines and workflow orchestration.
Data dictionary documentation and cloud object storage
design for data offload scenarios.
Collaboration with extended team like DevOps, Data
Architects, and operations teams to ensure end-to-end pipeline reliability and
observability.
Decisioning Platform (Pega or equivalent)
Demonstrate a sound understanding of how real-time
decisioning platforms (such as Pega) consume and depend on data engineering
outputs.
Work with decisioning and analytics teams to ensure
accurate and timely data availability for personalisation and customer
engagement use cases.
Participate in cross-team planning and technical reviews
as they relate to data dependencies for decisioning workflows.
Cross-Platform & Delivery
Deliver across multiple use cases simultaneously in an
Agile squad model, managing dependencies across platforms and teams.
Author and maintain technical documentation, including
solution design notes, implementation plans, and deployment runbooks.
Actively participate in sprint ceremonies, backlog
refinement, and cross-team dependency management.
Provide SIT, UAT, and data validation support across
environments; work with QA and operations teams to validate data integrity pre-
and post-release.
Skills & Experience
Familiarity with cloud-native data engineering patterns
and object storage-based data offload architectures (e.g. AWS S3, Azure Data
Lake).
Understanding of real-time decisioning platforms such as
Pega, and how they integrate with data engineering layers.
Exposure to MarTech or Transformation data programs in a
large enterprise or telco context.
Experience with workflow orchestration tools such as
Apache Airflow or equivalent.
Understanding of data governance frameworks, data
lineage, and data quality processes.