Senior Data Engineer - Hybrid

Why Join enablesGROUP?

Since 2016, enablesGROUP has been on a mission: to deliver high-quality operations and outsourcing services to every client, big or small.

Fast forward to 2026, we’ve grown our global footprint to serve 100+ clients and expanded into 4 key industries. At enablesGROUP, you’re not just joining a company – you’re joining a community that values growth, learning, and success. Check us out at www.enablesgroup.com.

At enablesGROUP, you’re not just joining a company – you’re joining a community that values growth, learning, and success.

We have market leading engagement scores and invest heavily in your Learning and Development, with a specific focus on enhancing your ability to leverage AI in your daily tasks.

Our Perks & Benefits include:

• Comprehensive health and life insurance starting Day 1, covering 2 eligible dependents.

• 20 leave credits for vacation, emergencies, sick days, and even your birthday! 🎉

• Endless opportunities for career advancement with annual performance reviews and salary increases.

• Company-provided laptop to set you up for success.

• Convenient office location in Pasig, at the heart of Manila, accessible to all.

• Loyalty rewards: Employees celebrating 5 years could receive a profit-sharing scheme.

• In-house learning & development programs with access to the latest in AI and technology.

Job Title: Senior Data Engineer
Location: Ortigas, Pasig, PH
Work Schedule: Monday to Friday, 3:00 PM to 1:00 AM PH Time (Hybrid | 3x Onsite, 2x WFH)

Job Summary

The Senior Data Engineer is a hands-on technical role responsible for designing, building, and maintaining scalable data pipelines and platform components that underpin enterprise data and analytics capabilities. Working within the Data Analytics & AI team, this individual will take ownership of core data engineering workstreams across the Databricks Lakehouse platform, ensuring data is ingested, transformed, and served reliably to support business intelligence, advanced analytics, and AI/ML use cases.

The ideal candidate is a skilled engineer with deep expertise in modern cloud data platforms, a strong command of SQL and Python, and a passion for building robust, well-tested data solutions. They bring software engineering discipline to data work embracing Infrastructure as Code, CI/CD, and version control as standard practice and thrive in a collaborative, cross-functional environment.

Job Responsibilities:

Data Pipeline Design & Development

• Design, develop, and maintain automated, scalable, and resilient data pipelines (ETL/ELT) within the Databricks Lakehouse platform, following the medallion architecture (bronze, silver, gold) to ensure data is progressively refined and business-ready.

• Build and optimise ingestion pipelines from a wide range of source systems, including relational databases (SQL Server, PostgreSQL), SaaS applications (Salesforce CRM, PeopleHR), APIs, flat files, and cloud storage, leveraging Azure Data Factory and Databricks Auto Loader.

• Develop transformation logic using SQL, Python, and PySpark to create high-quality, well-modelled data products that serve BI, analytics, and AI/ML consumers.

Data Platform Engineering & Operations

• Contribute to the architecture and continuous improvement of Databricks Lakehouse environment, including Unity Catalog for data governance, access control, and lineage tracking.

• Implement and maintain Infrastructure as Code (Terraform) for provisioning and managing platform resources, ensuring consistency and repeatability across environments.

• Embed CI/CD best practices using Azure DevOps and Git-based workflows for all pipeline and platform code, including automated testing, branching strategies, and peer code reviews.

• Monitor platform health, pipeline performance, and data quality, proactively identifying and resolving issues to maintain SLA adherence.

Data Modelling & Quality

• Conceptualise and implement logical and physical data models, including dimensional models (Kimball methodology), slowly changing dimensions, and conformed dimensions, to support enterprise reporting and analytics.

• Design and develop data quality frameworks, including validation rules, anomaly detection, and data profiling, to ensure the integrity and trustworthiness of data across the platform.

• Collaborate with the Data Governance lead to enforce data standards, cataloguing, and metadata management within Unity Catalog.

Collaboration & Continuous improvement

• Partner with BI analysts, data scientists, and business stakeholders to understand data requirements and translate them into well-defined, prioritised engineering deliverables.

• Constantly evaluate and adopt new technologies, tools, and patterns to optimise data flows, reduce costs, and improve platform performance.

• Contribute to internal knowledge sharing through documentation, code reviews, tech talks, and mentoring of junior team members.

• Support the Data Platform Manager in maintaining the platform roadmap and backlog, providing technical input and effort estimates for planned initiatives.

Qualifications:

• 4–5+ years' experience in data engineering, data platform development, or a closely related technical role required

• Strong proficiency in SQL and Python (including PySpark) for data transformation and pipeline development required

• Hands-on experience with Databricks (including Delta Lake, Unity Catalog, notebooks, workflows, and Auto Loader) required

• Solid experience with Azure cloud services (e.g., Azure Data Factory, Azure Data Lake Storage Gen2, Azure Key Vault, Azure DevOps) required

• Proven experience building and maintaining ETL/ELT pipelines at scale, with a strong understanding of the medallion architecture pattern required

• Experience with data modelling techniques, including dimensional modelling (Kimball methodology), star/snowflake schemas, and slowly changing dimensions required

• Strong software engineering fundamentals, including version control (Git), CI/CD pipelines, Infrastructure as Code (Terraform), automated testing, and code review practices required

• Familiarity with data governance concepts, including data cataloguing, lineage, and access control preferred

• Experience with orchestration and scheduling tools (e.g., Azure Data Factory, Databricks Workflows) required

• Exposure to BI and analytics tools (e.g., Power BI) and an understanding of how data is consumed downstream preferred

• Experience working within regulated industries (life sciences, healthcare, or pharma) preferred

• Strong analytical and problem-solving skills with the ability to work autonomously on complex technical challenges required

• Good written and verbal communication skills, with the ability to collaborate effectively across technical and non-technical teams required