Hiring! Senior Data Engineer

Ascendion; Makati; On-site; PHP 1,000,000 - 1,500,000; Full time

Job summary

Ascendion is seeking a data engineering professional to design, build, and support data integration pipelines and analytical assets for a risk-focused datamart. You will onboard new data domains and enable governed data consumption across reporting, analytics, and decision-support use cases.

You will craft reusable data products, develop Airflow DAGs, PySpark scripts, and manage data quality controls, partnering with analysts and stakeholders to deliver scalable data platforms on AWS.

Qualifications

• Experience building enterprise data pipelines and analytical data products.
• Experience with data warehouses, data lakes, data marts, and reporting platforms.
• Experience integrating assets into a standardized architecture.

Responsibilities

• Develop and maintain data ingestion, transformation, and data quality pipelines.
• Analyze and enhance existing data marts, analytical datasets, and reporting assets to improve data usability, consistency, and scalability.
• Build reusable data products, views, and curated datasets for enterprise consumption.
• Implement reconciliation, monitoring, audit, and data quality controls.
• Optimize data processing workflows and query performance.
• Support data platform modernization initiatives, including migration and rationalization of existing analytical assets.
• Collaborate with business analysts, architects, and stakeholders throughout solution delivery.
• Support testing, deployment, operationalization, and production support activities.

Skills

Python

PySpark

SQL

Airflow/MWAA

AWS services

Data modeling

Git and CI/CD

Data quality

Job description

Role Purpose

Design, develop, and support data integration, transformation, and analytical data products for a risk-focused datamart. The role will be responsible for onboarding new data domains, enhancing existing analytical assets, and enabling scalable, governed data consumption across reporting, analytics, and decision-support use cases.

Key Responsibilities

• Develop and maintain data ingestion, transformation, and data quality pipelines.
• Analyze and enhance existing data marts, analytical datasets, and reporting assets to improve data usability, consistency, and scalability.
• Build reusable data products, views, and curated datasets for enterprise consumption.
• Implement reconciliation, monitoring, audit, and data quality controls.
• Optimize data processing workflows and query performance.
• Support data platform modernization initiatives, including migration and rationalization of existing analytical assets.
• Collaborate with business analysts, architects, and stakeholders throughout solution delivery.
• Support testing, deployment, operationalization, and production support activities.

Expected Outputs

• Schema definitions for creating databases & tables on AWS S3 lakehouse
• Airflow DAGs
• Pyspark scripts for data ingestion

Required Skills

• ETL/ELT Development via Python, Pyspark, and SQL
• Data Orchestration via MWAA/Airflow DAGs
• AWS services
• Data Pipeline Development
• Data Quality and Reconciliation Frameworks
• Data Modeling Fundamentals
• Git and CI/CD Practices

Experience

• Experience building enterprise data pipelines and analytical data products.
• Experience working with data warehouses, data lakes, data marts, and reporting platforms.
• Experience reviewing, enhancing, and integrating existing enterprise analytical assets into a standardized architecture