We are looking for a Lead data engineer at Flip. You will be responsible for designing, building, and optimizing scalable data pipelines and infrastructure to support analytics, reporting, and real-time data use cases. You will also lead a small team of data engineers and help us identify and implement best practices in data architecture and cost-efficient processing, and drive adoption of open-source tools and modern data platforms.
About Flip
Rafi, Luqman, and Anjar, who were college friends in Universitas Indonesia, started Flip as a project in 2015 to transfer payments to each other at a fraction of what banks would charge them. They are pioneers in the Indonesian market, with their technology now helping millions of Indonesians, both individuals and businesses, carry out bank-to-bank money transfers through a reliable and seamless app.
After five years of operations, Flip has helped Indonesians transfer money worth several trillions of rupiah and has received double-digit funding from respectable investors such as Sequoia India, Insight Partner, and Insignia. Flip's ultimate mission is to give Indonesians access to one of the most progressive and fairest financial services in the world.
At Flip, we always strive to provide the fairest place for you to work, learn, and grow with talented and fun people in various opportunities to advance your career and get fair rewards. We believe that we have to treat employees, customers, and all stakeholders fairly and respectfully. Fair treatment for employees means we establish clear goals, facilitate our employees to achieve them, and value their contribution to the company with equitable benefits.
Key Responsibilities
- Design, build, and maintain scalable, cost-efficient data pipelines and models using tools such as dbt, SQL, and Python
- Architect and optimize data infrastructure with a focus on performance, reliability, and cost-effectiveness, especially in cloud environments like GCP
- Develop and maintain real-time and event-driven data pipelines using tools like Kafka, Pub/Sub, or Dataflow
- Collaborate with cross-functional teamsanalysts, product managers, and engineersto deliver clean, well-modeled data for analytics and operational use cases
- Implement and enforce data engineering best practices including testing, CI/CD for data, modular design, and observability
- Contribute to and maintain clear documentation, data lineage, and governance practices to ensure transparency and reproducibility
- Identify opportunities to reduce data platform costs through smarter storage, processing strategies, and architectural improvements
Requirements
- 5+ years of experience in Data Engineering or Analytics Engineering roles
- Strong proficiency in SQL, Python, and modern data transformation tools like dbt
- Solid understanding of data warehousing concepts and experience with BigQuery, Snowflake, or Redshift
- Hands-on experience with GCP data tools, including BigQuery, Pub/Sub, Dataflow, and DataStream are preferred
- Experience building and maintaining real-time or streaming data pipelines.
- Knowledge of open-source data tools (e.g., Debezium, Kafka, duckDB, Airflow, etc.)
- Demonstrated experience implementing cost-aware data solutions, including partitioning, clustering, and storage optimization
- Familiarity with CI/CD, version control, and production-grade deployment of data workflows
- Strong communication and collaboration skills across technical and business teams
Nice to Have
- People management experience
- Experience with data observability platforms (e.g., Monte Carlo, Datafold)
- Familiarity with Cube or other semantic layer modeling tools
- Exposure to ETL/ELT tools such as Pentaho, n8n, or SSIS
- Awareness of GCP cost optimization levers and alternatives to high-cost services
- Prior experience in fintech, marketplaces, or platform engineering environments
