Senior Data Engineer / Data Engineer
Job Profile Summary:
In this role, You will design, build and operate scalable ingestion and transformation workflows (batch and, where required, near real-time), implement standardized data models and a single view of customers/advisers, and enable analytics and reporting for distribution finance, commissions, advisory performance, and management insights. You will partner closely with business and technology stakeholders to ensure data quality, governance, security, and reliable delivery aligned to business requirements and IT standards.
Job Description:
- Build Data Solutions and Pipelines: Design, build, and optimize data solutions and curated datasets to support business use cases (e.g., distribution finance, commissions, adviser performance, customer insights, management reporting), ensuring reliability, scalability, and cost efficiency.
- Operate and Support Data Pipelines: Monitor, troubleshoot, and enhance production data workflows to meet agreed SLAs, including incident management, root-cause analysis, and continuous improvement.
- Controlled Data Extracts & Reporting Enablement: Respond to ad-hoc data requests and recurring reporting needs with speed and accuracy, while promoting reusable datasets, self-service patterns, and appropriate controls for sensitive financial and customer data.
- Governance, Security & SDLC Compliance: Deliver solutions in accordance with PACS SDLC, architectural standards, and security policies. Produce required artefacts (technical specifications, data models, data dictionary, interface and mapping specs, test plans). Implement and evidence data controls including data quality checks, reconciliation where applicable, access controls, and lineage/documentation.
- Stakeholder Management: Communicate proactively with both business and technology stakeholders surfacing risks, dependencies, and delivery timelines; managing queries and issues through to resolution.
- Cross-Functional Delivery: Partner with agile delivery teams, data analysts, and upstream system owners to source, integrate, and model data; drive delivery from requirements through build, testing, release, and post-production support.
Who we are looking for:
Competencies & Personal Traits:
- Self-Reliant and Adaptable: Thrives in a dynamic environment, demonstrating independence and resilience.
- Collaborative Team Player: Exhibits strong organizational skills and excels in managing interpersonal relationships.
- Analytical and Innovative: Possesses exceptional problem-solving abilities, with a flair for developing creative and effective solutions.
- Effective Communicator: Demonstrates excellent verbal and written communication skills, ensuring clarity and impact.
- Results-Driven and Customer-Focused: Committed to delivering outstanding results and exceeding customer expectations.
- Culturally Competent: Adept at working with a culturally diverse population, embracing inclusivity and collaboration.
- Technologically Savvy: Maintains a strong grasp of emerging technologies, competitive landscapes, and market trends.
- Data Engineering Fundamentals: Strong experience in building and optimizing ETL/ELT pipelines, data modelling (dimensional/normalized), and performance tuning for analytics workloads.
- Technical Proficiency: Experienced in one or more of the following technologies and practices:
- Azure data platform: Synapse, Data Lake (ADLS), Data Factory, Databricks; catalog/governance tools (e.g., Unity Catalog) and RBAC-based access controls
- PySpark / Python / SQL (Scala is a plus); data testing and data quality validation patterns
- Engineering toolchain: GitHub/Git, CI/CD pipelines, JIRA, Confluence
DevOps & Automation: Working knowledge of DevOps practices (version control, build/release automation, monitoring).
Working Experience
- 58 years of experience delivering enterprise data engineering solutions in cloud and big data environments, including production support and stakeholder-facing delivery.
Experience in insurance data domains is preferred. Exposure to Finance Advisory / distribution finance operations (e.g., commissions, adviser hierarchy, campaigns, remuneration, persistency) will be an added advantage.
Professional Qualifications
Certifications related to Data and Analytics would be an added advantage
Education
Master/bachelors degree in STEM (Science, Technology, Engineering, Mathematics)
Language
Fluency in written and spoken English
