Data Engineer: Build Scalable ETL & Analytics Pipelines
Summary
Data Engineer designing, building, and optimizing ETL pipelines, data models, and warehouse structures on cloud platforms (e.g., Azure Data Factory, Snowflake, Airflow) to support analytics, regulatory reporting, and business intelligence for a shared services or financial services organization in Taguig.
We are seeking a results-driven and detail-oriented Data Engineer to design, build, and maintain robust data pipelines and infrastructure supporting enterprise analytics and reporting. This role plays a key part in enabling high-quality data flows for operational decision-making, regulatory reporting, and business intelligence across a shared services or financial services organization.
The ideal candidate has strong experience with ETL development, data modeling, and cloud-based data platforms.
Key Responsibilities:
- Design, develop, and optimize data pipelines that extract, transform, and load (ETL) data from various internal and external sources.
- Work closely with data analysts, business users, and application teams to define data requirements and deliver scalable solutions.
- Ensure data quality, governance, and lineage through proper validation and monitoring tools.
- Implement data models and warehouse structures that support analytics, reporting, and compliance needs.
- Automate data integration workflows to support real-time and batch processing.
- Collaborate with infrastructure teams to ensure performance, security, and availability of data systems.
- Participate in code reviews, troubleshooting, and ongoing performance tuning.
- Maintain documentation of data architecture, lineage, and pipeline logic.
Qualifications:
- Bachelors degree in Computer Science, Information Systems, Engineering, or a related field.
- 3-5 years of experience in data engineering, ETL development, or enterprise data management.
- Proficiency in SQL and at least one programming language (e.g., Python, Java, Scala).
- Hands-on experience with data tools and platforms such as Azure Data Factory, AWS Glue, Snowflake, Databricks, or Apache Airflow.
- Solid understanding of data warehousing, dimensional modeling, and best practices in data governance.
- Familiarity with version control (e.g., Git), CI/CD pipelines, and Agile development methods.
- Strong problem-solving skills, attention to detail, and collaborative mindset.