Senior Data Engineer

Nezda Global; Taguig; On-site; PHP 1,800,000 - 3,000,000; Full time

Job summary

Nezda Global is seeking a data architecture leader to drive Client 360 capabilities with hands-on Python, Spark, and AWS expertise. You will own BAU operations, set quarterly release plans, and ensure governance and data quality across enterprise data platforms.

The role requires deep experience in data modelling, integration, MDM, and multi-vendor coordination, delivering scalable, secure cloud data solutions for business value.

Qualifications

• 10+ years in data, data engineering, data architecture, MDM, analytics.
• Experience in data modelling, integration architecture, data platforms.
• Strong AWS cloud technologies and data services experience.

Responsibilities

• Own BAU operations and roadmap for Client 360 capabilities.
• Define quarterly release plans and align with SLO/SLA targets.
• Lead architecture discussions and provide technical direction to teams.
• Ensure data governance, metadata, and data quality controls.
• Coordinate data integration across L&H and W&P domains.

Skills

Python

Apache Spark

AWS

Data Architecture

Data Engineering

Data Integration

MDM

Client 360

Data Governance

Data Quality

Tools

Python

Spark/PySpark

AWS

Job description

The role will provide strong Data Architecture and hands-on technical leadership, ensuring Client 360 solutions are scalable, reliable, secure, and aligned with Asia and Enterprise architecture standards. The position will work closely with business stakeholders, data governance teams, architects, technology partners, and delivery teams to drive data quality, integration, and business value.

The ideal candidate combines extensive technical experience in Python, Apache Spark, and AWS with strong expertise in Data Architecture, data engineering, data integration, MDM, and Client 360 solutions.

Key Accountabilities

• Own the BAU operations, enhancement backlog, and technology roadmap for Client 360 capabilities.
• Define and manage quarterly release plans, ensuring delivery against SLO/SLA targets.
• Identify opportunities to improve platform performance, scalability, reliability, and reduce technical debt.
• Drive continuous improvement and incremental value delivery to business stakeholders.
• Define and govern data architecture, data models, integration patterns, and target-state architectures for Client 360 capabilities.
• Ensure solutions align with Asia and Enterprise architecture standards, principles, and technology roadmaps.
• Design scalable data solutions across cloud, data platforms, analytical environments, and enterprise integration layers.
• Ensure effective integration with Master Data Management (MDM) and other enterprise data capabilities where applicable.
• Lead architecture discussions and provide technical direction to engineering and implementation teams.

3. Data Engineering & Technical Leadership

• Provide hands-on technical leadership in Python, Apache Spark/PySpark, and AWS for data processing, transformation, integration, and platform engineering.
• Design and review scalable batch and/or near-real-time data pipelines supporting Client 360 capabilities.
• Apply strong knowledge of AWS data services and cloud architecture to develop reliable and cost-effective data solutions.
• Conduct technical reviews, troubleshoot complex data issues, and provide root-cause analysis for production and data-quality incidents.
• Guide engineering teams and technology partners on coding standards, performance optimization, data processing, and solution design.
• Lead day-to-day engagement with consulting and technology partners.
• Review and validate Statements of Work (SoW), staffing requirements, deliverables, milestones, and technical outcomes.
• Coordinate with IT Service Management Office (SMO), Procurement, and vendors to ensure contractual and delivery commitments are met.
• Provide technical oversight of vendor-delivered solutions and ensure adherence to architecture and engineering standards.

5. Data Governance & Data Quality

• Embed data governance, metadata, data lineage, DGIA gates, and data quality controls into BAU and enhancement releases.
• Partner with the Business Data Governance Manager to establish and improve data quality standards and controls.
• Implement and monitor DQ scorecards, completeness, consistency, accuracy, and match-quality measures.
• Ensure governance artefacts are complete and accepted at required governance gates.

6. Data Integration & Client 360

• Coordinate data integration across L&H and W&P business domains.
• Ensure consistent and reliable customer data across source systems, enterprise data platforms, and Client 360 capabilities.
• Support the integration of customer, policy, product, claims, and other relevant insurance data domains.
• Apply knowledge of Customer 360, MDM, data integration, data modelling, and enterprise data architecture to improve customer data visibility and usability.

7. Data Analysis & Problem Resolution

• Analyze data originating from insurance core platforms and enterprise data sources.
• Perform complex data analysis and root-cause analysis to identify data, integration, and platform issues.
• Develop data remediation strategies and recommend sustainable technical solutions.
• Conduct cost-benefit analysis for remediation initiatives and technology improvements.

8. Stakeholder Management

• Facilitate prioritization, technical discussions, and sign-offs with business owners, architecture, compliance, governance, and technology teams.
• Communicate delivery status, technical risks, dependencies, financials, and benefits realization.
• Translate complex technical and data architecture concepts into actionable recommendations for business stakeholders.

Qualifications & Experience

• 10+ years of experience in Data, Data Engineering, Data Architecture, MDM, Analytics, or related technology delivery roles.
• Strong experience in Data Architecture and enterprise data solution design, including data modelling, integration architecture, data platforms, and architecture governance.
• Extensive hands-on technical experience in Python and Apache Spark/PySpark, including development and optimization of large-scale data processing and transformation pipelines.
• Strong experience with AWS cloud technologies and data services, with the ability to design and implement scalable, secure, and cost-efficient cloud data solutions.
• Proven experience in Data Engineering, including ETL/ELT, data pipelines, batch/stream processing, data integration, and production data operations.
• Strong understanding of Customer 360, Master Data Management (MDM), data integration, data quality, metadata, and data lineage.
• Experience working with complex enterprise data environments, preferably within insurance, financial services, or other highly regulated industries.
• Experience integrating Data Governance, CDE, DGIA, DQ, metadata, and lineage requirements into technology products and delivery processes.
• Strong experience in multi-vendor management, including SoW governance, commercial management, delivery oversight, and partner coordination.
• Demonstrated experience leading technical teams, architects, engineers, and/or technology partners.
• Strong analytical and problem-solving skills, with experience performing data root-cause analysis and developing data remediation plans.
• Excellent stakeholder management and communication skills, with the ability to work effectively across business, technology, architecture, governance, and compliance teams.