Staff Data Engineer (Managerial Level)

About us:  

Populix is a consumer insights platform that helps businesses connect with its database of respondents and provides them with insights to better understand the preferences of Indonesian consumers. Populix has a pool of over 1,000,000 diverse, readily accessible, and highly qualified respondents across Indonesia. Its products range from intensive research studies to simple surveys and can be arranged on a project or subscription basis. Focusing on Indonesian consumers being super sticky to their phones, Populix facilitates a diverse range of data collection methods via its mobile app.

Populix is building the future of AI-powered market research, combining structured data, unstructured insights, and generative AI into a seamless research intelligence platform. As a Senior/Staff Data Engineer, you will be a technical leader responsible for designing, building, and maintaining scalable, reliable, and secure data pipelines and infrastructure. You will work closely with cross-functional teams including Product, Data Science, and Engineering to ensure high-quality, timely data powers business decisions across the organization.

This is a hands-on leadership role: you will write production-grade code, architect data solutions, mentor team members, and drive best practices in data engineering, governance, and security.
 

Key Responsibilities :

• Design, build, and maintain production-grade ETL/ELT data pipelines using Python/Spark/Rust for data processing.
• Build and optimize data transformations and aggregations for analytical workloads, including complex SQL with CTEs, window functions, and analyzing query execution/query planner.
• Develop and manage workflow orchestration using Apache Airflow/Dagster/Prefect (or other orchestration tools).
• Architect and maintain cloud-based data infrastructure on Google Cloud Platform (GCP), leveraging services such as BigQuery, Dataproc, Dataflow, Google Cloud Storage, Pub/Sub, Compute Engine, and Cloud Functions for Data and Machine Learning workload.
• Design data models and schemas that support OLAP analytics, reporting, and downstream machine learning use cases.
• Implement data quality checks, validation frameworks, monitoring, and alerting for pipeline health and data integrity.
• Ensure data systems comply with security standards, including ISO 27001, SOC Type 2, data privacy regulations like UU PDP, and internal information transfer policies.
• Create and maintain comprehensive documentation for data pipelines, data models, infrastructure, and operational procedures.
• Lead cross-functional collaboration with Data Analysts, Data Scientists, Product Managers, and Software Engineers to translate business requirements into scalable data solutions.