Data Architect (Mid-Senior)

Job Description

At Umpisa Inc., our mission is to make the Philippines be known globally as a tech hub. 

Umpisa Inc. is a progressive technology services company that partners with select industries, clients and people to work on pioneering and industry-changing solutions via digital transformation, modern software development and venture building.

We create a set of world-class and impactful products and solutions to help organizations and individuals live better lives. We offer demanding, challenging and rewarding careers in software development, product development, emerging technologies, and more for the right candidates.

Essential Skills:

• Aligns with our values: Excellence, Integrity, Professionalism, People Success, Customer Success, Fun, Innovation and Diversity
• Strong communication skills
• Strong problem solving and analytical skills
• Excellent problem-solving ability
• Would like to work as part of a self-organizing Scrum team in a scaled agile framework
• Must be a self-starter and loves to collaborate with the team and client

Job Summary

The Data Architect will be responsible for designing, building, and managing the organization’s data architecture, ensuring the efficient flow and accessibility of data across systems. You will work closely with cross-functional teams to define data strategies, create data models, and establish standards and best practices for data governance. This role requires a deep understanding of data systems, cloud technologies, and database design, along with strong communication skills to collaborate with business stakeholders and technical teams.

Requirements

Key Responsibilities:

• Data Architecture Design: Design and build scalable, flexible, and efficient data architectures that support business operations and future growth. Define database structures, data warehouses, and data lakes based on business requirements.
• Data Modeling: Create conceptual, logical, and physical data models that provide a clear and consistent structure for storing, retrieving, and using data across systems. Ensure data models align with the organization’s business and technical needs.
• Data Integration: Oversee data integration strategies, ensuring seamless data flow between internal systems, third-party applications, and cloud platforms. Implement ETL/ELT processes to ensure the data is properly transformed and integrated.
• Data Governance & Quality: Establish and enforce data governance policies and practices to ensure data quality, consistency, security, and compliance across the organization. Collaborate with the data governance team to implement data standards, best practices, and data stewardship.
• Cloud & On-Premise Architecture: Design and implement data solutions that integrate cloud (e.g., AWS, Azure, Google Cloud) and on-premise data systems. Leverage cloud data platforms like Snowflake, Redshift, or BigQuery to build scalable and efficient data environments.
• Security & Compliance: Ensure that data architectures are secure and compliant with industry regulations, such as GDPR, CCPA, and other data privacy laws. Work closely with IT and security teams to implement encryption, access controls, and other data protection measures.
• Collaboration with Stakeholders: Partner with business leaders, data engineers, data scientists, and other technical teams to understand data requirements, set data priorities, and provide data solutions that meet the company’s strategic goals.
• Cloud & On-Premise Architecture: Design and implement data solutions that integrate cloud (e.g., AWS, Azure, Google Cloud) and on-premise data systems. Leverage cloud data platforms like Snowflake, Redshift, or BigQuery to build scalable and efficient data environments.
• Security & Compliance: Ensure that data architectures are secure and compliant with industry regulations, such as GDPR, CCPA, and other data privacy laws. Work closely with IT and security teams to implement encryption, access controls, and other data protection measures.
• Collaboration with Stakeholders: Partner with business leaders, data engineers, data scientists, and other technical teams to understand data requirements, set data priorities, and provide data solutions that meet the company’s strategic goals.

Minimum Requirements:

• Trustworthy, self-motivated, responsive and sharp
• Strong listening, verbal, written and presentation communication skills are essential
• Excellent attention to detail
• Strong communication skills, organization skills, time management skills

Preferred Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Data Science, or a related field. A Master’s degree or relevant certifications (e.g., AWS Certified Big Data – Specialty) is a plus.
• 3-5+ years of experience in data architecture, data engineering, or a related field, with a strong background in designing and implementing data systems and data modeling.
• Extensive experience with relational and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB, Cassandra).
• Expertise in data warehousing, data lakes, and cloud-based data solutions (e.g., AWS Redshift, Google BigQuery, Snowflake).
• Proficiency in data modeling techniques and tools (e.g., ERwin, IBM InfoSphere).
• Strong understanding of ETL/ELT processes and integration platforms (e.g., Apache Kafka, Apache NiFi, Talend).
• Experience with cloud platforms (e.g., AWS, Azure, Google Cloud), including cloud-native data solutions (e.g., S3, Lambda, BigQuery).
• Familiarity with big data technologies like Hadoop, Spark, or similar distributed data frameworks.
• Knowledge of data privacy regulations (GDPR, CCPA) and industry best practices in data security.
• Experience with DevOps practices and CI/CD pipelines for data infrastructure is a plus.
• Strong problem-solving skills, with the ability to architect complex systems and data solutions.
• Deep understanding of performance tuning, query optimization, and data storage.
• Ability to communicate complex technical concepts clearly to both technical and non-technical stakeholders.
• Excellent documentation skills to ensure clear and accessible data architecture designs.
• Ability to lead and mentor teams of data engineers, promoting best practices and high standards of data design.