Senior Analytics Engineer

SKINTIFIC; Jakarta Selatan; On-site; IDR 400,000,000 - 650,000,000; Full time

Job summary

SKINTIFIC is seeking a Senior Analytics Engineer to turn data from multiple systems into trusted datasets and scalable reporting models that support sales, product performance, and inventory decisions.

The role blends data engineering, analytics, and business insight, requiring strong SQL and data modeling, hands-on Power BI, Python, automation, and cloud data warehouse experience. You will collaborate with stakeholders to deliver reliable, trusted metrics.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• Experience owning production data models and reporting datasets.
• Strong analytical thinking and focus on data accuracy.
• Ability to translate ambiguous business needs into practical data solutions.
• Professional English proficiency, written and spoken.

Responsibilities

• Design, build, and maintain reusable data models and reporting datasets.
• Integrate data from multiple systems and resolve classification differences.
• Translate reporting needs into clear metrics and data requirements.
• Develop and maintain Power BI semantic models, measures, and relationships.
• Build automated data preparation and quality checks.
• Document data models, metric definitions, and transformation logic.
• Collaborate with data engineers and analysts to troubleshoot production data issues.
• Apply AI-assisted tools judiciously for development and testing.

Skills

Advanced SQL

Data modeling

Power BI

DAX

Python

ETL/ELT

Cloud data warehouses

Git / code review

Excel & Google Sheets

Education

Bachelor's degree in CS/IS/DS/Engineering

Tools

BigQuery

dbt

Airflow

Job description

SENIOR ANALYTICS ENGINEER

Location: Mega Kuningan, South Jakarta

Work Arrangement: On-site

ABOUT THE ROLE

• We are looking for a Senior Analytics Engineer to help turn data from multiple systems into trusted datasets, consistent business metrics, and scalable reporting models that support sales, product performance, and inventory decisions.
• This role sits at the intersection of data engineering, analytics, and business, combining strong SQL and data modeling skills with hands‑on Power BI, Python, automation, and cloud data warehouse experience.
• You’ll work closely with business stakeholders, data engineers, and analysts to understand business needs, build reliable data solutions, and ensure that the numbers used for decision‑making are accurate, consistent, and easy to trust.
• We’re looking for someone who takes ownership, works independently, challenges unclear requirements, and proactively ensures data reliability — not someone who waits to be chased when something breaks.
• You’ll also have the opportunity to use AI tools and agents to improve development, testing, documentation, and analysis, while applying sound judgment to validate AI‑generated outputs.

KEY RESPONSIBILITIES

• Design, build, and maintain reusable data models and reporting datasets.
• Integrate data from multiple systems and resolve differences in identifiers, classifications, formats, and business definitions.
• Partner with business stakeholders to translate reporting needs into clear metrics and data requirements.
• Develop and maintain Power BI semantic models, measures, relationships, and reporting logic to ensure consistent calculations.
• Build automated processes for data preparation and quality checks, including accuracy, completeness, consistency, and freshness.
• Manage late‑arriving data, corrections, and historical changes while maintaining data traceability.
• Improve query performance, data reliability, and usability.
• Implement approved data access controls and row‑level security where required.
• Document data models, metric definitions, source relationships, and transformation logic.
• Collaborate with data engineers and analysts to deliver maintainable solutions and troubleshoot production data issues.
• Use AI‑assisted tools responsibly for development, testing, documentation, and analysis — while critically reviewing and validating the outputs.

REQUIREMENTS

• Bachelor’s degree in Computer Science, Information Systems, Data Science, Engineering, or a related field — or equivalent practical experience.
• Proven experience in Analytics Engineering, BI Engineering, Data Engineering, or a related role.
• Experience owning production data models and reporting datasets.
• Strong analytical thinking, problem‑solving skills, and a strong focus on data accuracy.
• Ability to translate ambiguous business needs into practical data solutions.
• Strong communication skills and the ability to explain technical concepts and data limitations to non‑technical stakeholders.
• Comfortable working independently, managing priorities, and collaborating across teams.
• Professional English proficiency, both written and spoken.
• Comfortable learning and applying AI tools with good judgment when reviewing their outputs.

TECHNICAL SKILLS

Required:

• Advanced SQL, including complex joins, window functions, incremental processing, and query optimization.
• Strong data modeling skills, including fact/dimension design, historical changes, and datasets at different levels of granularity.
• Strong hands‑on experience with Power BI, including semantic modeling, DAX, relationships, filter context, and Row‑Level Security (RLS).
• Proficiency in Python for data processing, automation, and validation.
• Experience with ETL/ELT workflows and automated data quality checks.
• Experience working with cloud data warehouses.
• Working knowledge of Git, code review, testing, documentation, and controlled deployment.
• Comfortable working with Excel and Google Sheets as data sources and transforming recurring spreadsheet processes into scalable solutions.

Preferred:

• Experience with GCP / BigQuery.
• Experience with dbt or an equivalent transformation framework.
• Familiarity with orchestration tools such as Airflow.
• Experience with AI‑assisted development and workflow automation.

BUSINESS & ANALYTICS MINDSET

Beyond technical skills, you should be someone who:

• Enjoys understanding why the business needs the data, not just how to retrieve it.
• Can learn unfamiliar business processes and clarify terminology with stakeholders.
• Understands how metric definitions, reporting periods, missing data, and source limitations can affect business decisions.
• Can reconcile different stakeholder requirements and help establish shared definitions and trusted metrics.
• Takes ownership of data quality and proactively identifies issues before they impact reporting.
• Focuses on building solutions that are reliable,