Data Analyst

ING Hubs Philippines; Metro Manila; In loco; PHP 480.000 - 900.000; Tempo pieno

Descrizione del lavoro

ING Hubs Philippines is seeking a Data Analyst to safeguard the accuracy and reliability of large datasets. You will develop scalable data-quality capabilities and automated validation frameworks, including anomaly detection, without needing domain knowledge.

You will collaborate with data engineering, product, and reporting teams to ensure trustworthy data for operational, analytical, and AI-driven systems.

Competenze

• Experience with SQL for data extraction, cleaning and validation.
• Knowledge of data-quality frameworks and statistical validation.

Mansioni

• Validate structured and unstructured datasets for completeness and accuracy.
• Design automated data-validation checks and monitoring.
• Develop scripts and pipelines to automate quality assessments.

Conoscenze

SQL

Python/R

data-quality

anomaly detection

ETL/ELT

GenAI concepts

LLM automation

Strumenti

SODA

data pipelines

monitoring tools

Copilot/MS Copilot

Descrizione del lavoro

Role Summary

The Data Analyst plays a critical role in safeguarding the accuracy, integrity, and reliability of large and complex datasets. This role focuses on developing scalable data‑quality capabilities and automated validation frameworks and intelligent anomaly detection — without requiring domain knowledge of the underlying business data.

Job Title: Data Analyst

Role Summary

The Data Analyst plays a critical role in safeguarding the accuracy, integrity, and reliability of large and complex datasets. This role focuses on developing scalable data‑quality capabilities and automated validation frameworks and intelligent anomaly detection — without requiring domain knowledge of the underlying business data.

You will collaborate closely with data engineering, product, and reporting teams, ensuring that data feeding our operational, analytical, and AI‑driven systems is trustworthy, consistent, and ready for decision‑making. The ideal candidate combines technical depth with strong analytical intuition and thrives in data‑intensive, fast‑moving environments.

Key Responsibilities

Data Quality & Validation

• Perform in depth validation of structured and unstructured datasets to ensure completeness, consistency, accuracy and lineage integrity.
• Apply statistical techniques, pattern recognition methods, and anomaly detection algorithms to identify irregularities without requiring domain‑expertise.
• Design and implement automated data‑validation checks, including schema validation, threshold monitoring, drift and distribution detection, and rules‑based and machine learning based assessments.

Tooling & Automation

• Develop scripts, tools, and data pipelines to automate data‑quality assessments.
• Build reusable frameworks to detect data inconsistencies across multiple sources and formats.
• Integrate validation tools into existing data infrastructure (e.g., ETL/ELT pipelines, data warehouses, APIs and event driven architectures).

Required Skills & Qualifications

Technical Skills

• Strong proficiency in SQL (data extraction, cleaning, and validation).
• Experience with Python or R for data processing, automation, and tool development.
• Familiarity with data‑quality frameworks, anomaly detection techniques, and statistical validation.
• Experience working with large datasets and modern data‑platform technologies.
• Knowledge of data‑integration patterns (ETL/ELT) and monitoring tools.
• Familiar with GenAI concepts and prompt engineering and LLM assisted automation
• Experienced in building agents with MS Copilot studio or comparable technology

Analytical Skills

• Excellent problem‑solving skills — especially in contexts where domain knowledge is limited.
• Ability to identify trends, irregularities, and outliers using structured and unstructured methods.
• Strong logical reasoning, abstraction and hypothesis driven thinking
• Demonstrate pattern‑recognition abilities, translating data signals into actionable insights.

Preferred Qualifications

• Experience with data‑quality monitoring tools (i.e. SODA).
• Familiar with data engineering concepts