Data Engineer

BDO Unibank; Metro Manila; On-site; PHP 900,000 - 1,500,000; Full time

Job summary

BDO Unibank is seeking a Data Engineer to design, develop, and maintain end-to-end data platforms and pipelines in Makati. You will build scalable, cloud-native data solutions using Azure and Databricks, ensuring data quality and governance across Bronze to Gold layers.

You will collaborate with data scientists, analysts, and stakeholders to enable analytics and ML initiatives, implement CI/CD for data workloads, and deliver observability dashboards and runbooks for platform reliability.

Qualifications

• Bachelor’s degree in computer science, engineering, or a related field.
• 3–5 years of experience in data engineering, analytics engineering, or related software roles.
• At least 2 years of Databricks and Azure Data Platform experience.
• Willing to work onsite in Makati.

Responsibilities

• Design, develop, and maintain end-to-end data pipelines and analytic apps.
• Implement Lakehouse architectures (Bronze/Silver/Gold) with Delta Lake.
• Build scalable, secure, and compliant cloud-native data solutions on Azure.
• Collaborate with cross-functional teams and support data science initiatives.
• Develop data quality validation, lineage, and observability solutions.
• Define CI/CD for data pipelines and automate testing and deployments.

Skills

Databricks

Azure Data Platform

Data pipelines

Data modeling

ETL design

Data governance

Data quality

DevOps for data

Spark tuning

Python/Scala

Education

Bachelor’s degree in computer science, Engineering, or related field

Tools

Azure Data Factory

Azure Data Lake Storage Gen2

Azure Synapse Analytics

Delta Lake

Medallion Architecture

Databricks

Microsoft Fabric

Job description

As a Data Engineer, you will play a key role in designing, developing, and maintaining end-to-end data solutions in the Enterprise Data Platform. You will be responsible for building scalable, efficient, reliable, and reusable data pipelines and analytic applications and your expertise will be leveraged in both backend and frontend development. You will collaborate closely with cross-functional teams to understand business requirements, architect data solutions, and implement best practices for data engineering and analytics.

Key Responsibilities:

Data Design, ingestion, Integration, and Processing

• Design, develop, and implement data solutions including data ingestion pipelines to acquire data from various source systems, storage processing, and visualization as needed, leveraging Azure Data Platforms and Databricks.
• Design and implement Lakehouse architectures using Delta Lake and Medallion Architecture principles (Bronze, Silver, Gold layers) to support scalable and governed data processing.
• Design and define data models, schemas, and structures to support analytical and reporting needs, ensuring scalability, performance, and data integrity.
• Design secure, scalable, and resilient cloud-native data architectures leveraging Azure services such as Azure Data Factory, Azure Data Lake Storage Gen2, Azure Synapse Analytics, Azure Event Hub, Azure Functions, and Microsoft Fabric where applicable.
• Configure and manage data storage solutions to store structured, semi-structured, and unstructured data. Optimize data storage and retrieval mechanisms for performance, cost efficiency, and compliance with data governance standards and regulations.
• Designing and implementing data integration/ingestion/transformation/migration/extraction which includes effectively mapping and converting data from source systems to target systems, ensuring precision and uniformity of data migration and integration.
• Develop and implement data quality validation frameworks, reconciliation processes, and exception handling mechanisms to ensure accuracy, completeness, consistency, and timeliness of enterprise data.
• Design and implement batch and real-time data ingestion solutions using event-driven and streaming architectures where required by business use cases.
• Design, upgrade, and implement new data workflows, automation, tools, and API integrations.

Implementation Expectations

• Perform unit and integration testing. Provide support in UAT, user training, pre-implementation and post-implementation support activities.
• Provide support/ executive analysis needs including ad-hoc reporting analysis, dashboard creating, design of data models, etc.
• Collaborate with data scientists, analysts, and other stakeholders to support their data needs and to ensure that data is properly integrated and aligned with business requirements.
• Support AI, Machine Learning, and Advanced Analytics initiatives by delivering trusted, governed, and reusable datasets across the enterprise.
• Create technical documents such as solution design, program specifications, and other required documentation in the SDLC process.
• Maintain technical documentation including architecture diagrams, data lineage documentation, operational runbooks, and data dictionaries.

DevOps and Automation

• Implement DevOps practices and automation workflows to streamline the development, deployment, and monitoring of data pipelines and applications.
• Collaborate with DevOps team to define CI/CD pipelines, automate testing, and ensure reliability and scalability of data solutions.
• Utilize source control, branching strategies, Infrastructure as Code (IaC), and release management practices to enable automated and repeatable deployments

Performance Optimization and Tuning

• Monitor and optimize the performance of data pipelines and analytics applications, identifying bottlenecks, optimizing query performance, and tuning Spark jobs for efficiency.
• Implement caching, partitioning, indexing, and other strategies to improve data processing speed and reduce latency in workloads.
• Monitor platform resource utilization and recommend optimization opportunities to improve performance, scalability, and cloud cost efficiency.

Controls and Compliance

• Implement security controls and encryption mechanisms to protect sensitive data stored and processed in the platform.
• Ensure compliance with industry standards, bank-wide standards, and other regulatory requirements.
• Support enterprise data governance initiatives through metadata management, data cataloging, lineage tracking, data classification, retention policies, and access control implementation.

Data Quality and Observability

• Implement data observability and monitoring solutions to proactively identify data quality issues, data drift, schema changes, pipeline failures, and service degradation.
• Develop and maintain operational dashboards, alerts, and monitoring metrics to ensure platform reliability and data freshness.
• Establish Service Level Objectives (SLOs) and Service Level Agreements (SLAs) for mission-critical data pipelines and services.

Others

• Provide support on maintenance and operations of the environment as needed (e.g. updates, patches)
• Collaborate with third-party vendors, technology partners, and internal teams to support successful project delivery, issue resolution, and platform enhancements.
• Conduct knowledge sharing sessions and training workshops to educate team members and other stakeholders.
• Stay up to date with industry developments and trends and recommend and implement new technologies and approaches to improve data engineering processes.

Qualifications:

• Bachelor’s degree in computer science, Engineering, or related field
• Minimum 3-5 years of experience in Data Engineering, Data Warehousing, Analytics Engineering, or related software engineering roles.
• At least 2 years' experience in Databricks and Microsoft Azure Data Platform
• Must be willing to work onsite and be assigned in Makati