• Lead end-to-end data architecture design and implementation for large-scale modernization and digital transformation initiatives.

• Design, develop, and maintain scalable data pipelines for data generation, collection, integration, and processing.

• Build modern data platforms using cloud services such as Azure, AWS, or GCP.

• Design and implement data lakehouses, streaming pipelines, and orchestration frameworks.

• Drive the adoption of modern architecture patterns, including Data Mesh, Data Fabric, Data Virtualization, and Knowledge Graphs.

• Work with modern data technologies such as Apache Spark, Databricks, Snowflake, Azure Synapse, BigQuery, Kafka, dbt, and Delta Lake.

• Develop logical and physical data models for structured, semi-structured, and unstructured data.

• Implement ETL/ELT processes to migrate, transform, and deploy data across systems.

• Establish data governance, metadata management, master data management, data quality, security, and access control standards.

• Collaborate with data engineers, product owners, and business stakeholders to translate business requirements into scalable technical solutions.

• Evaluate and recommend tools, platforms, and frameworks aligned with enterprise data strategy.

• Implement DevOps, DataOps, and CI/CD practices for data pipelines.

• Support AI/ML platform integration and enable advanced analytics capabilities.

Required Skills and Qualifications

• Minimum 5 years of relevant experience in Data Architecture and Engineering, with 10+ years preferred.

• Hands-on experience with at least one major cloud platform: Azure, AWS, or GCP. Experience with emerging platforms such as IOMETE is an advantage.

• Strong knowledge of modern data warehousing and lakehouse platforms, including Snowflake, Databricks, Azure Synapse, Amazon Redshift, or BigQuery.

• Experience with ETL/ELT tools and frameworks such as dbt, Azure Data Factory, AWS Glue, or Apache Airflow.

• Experience with real-time data processing and streaming technologies such as Apache Kafka and Spark Structured Streaming.

• Strong understanding of data modeling, data quality frameworks, metadata management, and data integration.

• Familiarity with Data Mesh, Data Fabric, and data product concepts.

• Experience with data cataloging and discovery tools such as Collibra, Alation, or Microsoft Purview.

• Knowledge of DevOps, DataOps, CI/CD, and data pipeline automation.

• Experience integrating data platforms with AI/ML solutions.

• Excellent communication, problem-solving, and stakeholder management skills.

Recruitment and Screening Process

• Initial screening through the CV Reviewer.

• Endorsement for validation and further CV screening through Workday, along with the HRI toolkit, FCV, and CV.

• Final review with the client.

Pre-Screening Questions

1. How many years of relevant hands-on experience do you have in Data Architecture and Engineering?

2. What was your last drawn salary?

3. What is your expected salary?

4. Are you willing to work onsite in Manila, Philippines, on a shifting schedule?

Important Notes for Recruiters

• Do not mention the client’s name in the job posting.

• Upload the candidate’s latest CV in PDF format.

• Include screening notes based on the job description before submitting the candidate for further evaluation.