Junior Data Engineer

Job Overview Aboitiz Data Innovation (ADI) is the Data Science and Artificial Intelligence arm of the Aboitiz Group. We believe data can drive change for a better world by advancing businesses across industries and communities. We are looking for a motivated Junior Data Engineer to join our growing Data Engineering team. In this role, you will assist in building, optimizing, and maintaining scalable data pipelines and infrastructure. You will work closely with Data Scientists, Analysts, and Senior Engineers to process raw data into actionable insights that power ADI’s AI and advanced analytics solutions. If you are excited to work in a fast-paced start-up environment, this role will provide a great opportunity to grow your career with us. Responsibilities Data Pipeline Construction: Assist in developing, testing, and deploying robust ETL/ELT pipelines to integrate data from various source systems into centralized data repositories. Database Maintenance: Write efficient SQL queries, create views, and maintain database schemas to ensure optimal performance and accessibility. Data Quality & Governance: Implement automated data validation routines and quality checks to ensure data accuracy, consistency, and completeness. Collaboration: Partner with Data Scientists, Software Engineers, and Business Analysts to understand data requirements and translate them into efficient data structures. Documentation & Monitoring: Document data models, pipeline architectures, and data flows; assist in monitoring running jobs and troubleshooting operational issues. Requirements and Qualifications Essential Qualifications (Required) Education: Bachelor’s degree in Computer Science, Information Technology, Data Engineering, Software Engineering, or a related quantitative field (or equivalent hands-on experience). Programming Skills: Strong proficiency in Python for data manipulation, scripting, and pipeline automation (familiarity with libraries like pandas , pyspark , or numpy ). Relational Databases & SQL: Advanced proficiency in SQL with the ability to write complex queries, joins, aggregations, and performance-tuned scripts. Core Fundamentals: Solid understanding of core software engineering principles, version control, data modeling concepts, and database architectures (relational and non-relational). Problem Solving: Strong analytical mindset, attention to detail, and a proactive approach to troubleshooting data pipeline failures or anomalies. Communication: Good verbal and written communication skills to collaborate effectively across multidisciplinary teams. Preferred Qualifications (Nice-to-Have) Cloud Platforms: Hands-on exposure to cloud data solutions, specifically AWS Lakehouse / Analytics Engines: Experience with Databricks and Apache Spark (PySpark) for large-scale data processing. Workflow Orchestration: Familiarity with DAG-based orchestration tools Modern Data Stack: Familiarity with modern data tools such as dbt , Snowflake, or BigQuery.