Data Engineer - Data Architecture

Stafflink Express; Manila; On-site; PHP 2,400,000 - 4,200,000; Full time

Job summary

Stafflink Express seeks an experienced Data Architect to design, develop and maintain data solutions for data generation, collection, and processing. Lead the design of next‑generation data platforms and architect scalable, cloud‑native, real‑time data ecosystems to support analytics and AI initiatives.

You will collaborate with data engineers, product owners, and stakeholders to translate requirements into scalable data solutions, define data governance and security strategies, and evaluate

Qualifications

• 10+ years of experience in data architecture and engineering roles.
• Deep knowledge of modern data warehouses and lakehouse platforms.
• Hands-on cloud experience (Azure, AWS or GCP).
• Experience with data integration, ETL/ELT tools.
• Familiarity with data mesh, data fabric, and data product concepts.
• Experience with data governance, metadata management, and security.
• Experience with streaming and real-time data platforms.
• Knowledge of DevOps, DataOps, and CI/CD for data pipelines.
• Experience with AI/ML platform integration.

Responsibilities

• Lead end-to-end data architecture for large-scale modernization and digital transformation programs.
• Design and implement cloud-native data platforms with lakehouses, streaming pipelines, and orchestration frameworks.
• Define and drive adoption of modern architecture patterns such as data mesh, data fabric, data virtualization, and knowledge graphs.
• Drive the adoption of modern data stacks: Spark, Databricks, Snowflake, Synapse, BigQuery, Kafka, dbt, Delta Lake, etc.
• Develop logical and physical data models to support structured, semi-structured, and unstructured data.
• Collaborate with data engineers, product owners, and business stakeholders to translate requirements into scalable data solutions.
• Define and implement strategies for data governance, metadata management, master data management, data security and access controls.
• Evaluate and recommend tools, platforms, and frameworks aligned with enterprise data strategy.

Skills

Data architecture

Cloud platforms

ETL/ELT

Data modeling

Data governance

Data quality

Data mesh

CI/CD for data

Streaming data

AI/ML integration

Tools

dbt

Azure Data Factory

AWS Glue

Airflow

Kafka

Spark

Databricks

Snowflake

Synapse

Job description

About the role

Design, develop and maintain data solutions for data generation, collection, and processing. Create data pipelines, ensure data quality, and implement ETL (extract, transform and load) processes to migrate and deploy data across systems. Lead the design and implementation of next-generation data platforms. Architect scalable, cloud-native, and real-time data ecosystems to support advanced analytics, AI/ML, and digital transformation initiatives.

Key responsibilities

• Lead end-to-end data architecture for large-scale modernization and digital transformation programs.
• Design and implement modern data platforms using cloud-native services (Azure, AWS, GCP) including lakehouses, streaming pipelines, and orchestration frameworks.
• Define and drive adoption of modern architecture patterns such as data mesh, data fabric, data virtualization, and knowledge graphs.
• Drive the adoption of modern data stacks: Spark, Databricks, Snowflake, Synapse, BigQuery, Kafka, dbt, Delta Lake, etc.
• Develop logical and physical data models to support structured, semi-structured, and unstructured data.
• Collaborate with data engineers, product owners, and business stakeholders to translate requirements into scalable data solutions.
• Define and implement strategies for data governance, metadata management, master data management, data security and access controls.
• Evaluate and recommend tools, platforms, and frameworks aligned with enterprise data strategy.

About you

• 10+ years of experience in data architecture and engineering roles.
• Hands-on experience with at least one major cloud platform (Azure, AWS, GCP) or emerging platforms like IOMETE.
• Expertise in modern data warehousing and lakehouse platforms: Snowflake, Databricks, Synapse, Redshift, BigQuery.
• Deep knowledge of data integration, ETL/ELT tools (e.g., dbt, Azure Data Factory, Glue, Airflow).
• Experience with streaming and real-time data platforms like Kafka, Spark Structured Streaming.
• Strong understanding of data modeling, data quality frameworks, and metadata management.
• Familiarity with data mesh, data fabric, and data product concepts.
• Experience with data cataloging and discovery tools: Collibra, Alation, Purview, etc.
• Knowledge of DevOps, DataOps, and CI/CD for data pipelines.
• Experience with AI/ML platform integration.