Lap Labs' unique data platform becomes the strongest basis of an AI Native organization.
: We are looking for a Data Engineer to join this exciting mission!
[Let me introduce the organization you will join 🚀]
As the AI era arrives, the value of 'good data' has greatly increased. Especially since Lap Labs aims to be an AI Native organization, all members resonate with that value. The data engineering team at Lap Labs has the mission of continuously producing, managing, and providing this good data. Therefore, we design data pipelines by deeply considering not only the physical characteristics of each data, the domain context, but also the needs of colleagues who utilize it. Currently, the data works as the growth engine for Quinit and PaldoGam, and we are looking for someone to evolve the supporting data platform to a higher level. You will experience constant consideration and development to ensure that data is provided in its most accurate form at the moment it is most needed within the rapidly growing commerce platform.
[The technology stack at Lap Labs 🚀]
•Data Warehouse: BigQuery
•Workflow: Airflow
•Streaming: Kafka, Kafka Connect, Debezium
•Language: Python
•Infrastructure: Kubernetes, Terraform
•Cloud: AWS, GCP
•Data Quality: Dataform
[If you join, you will work on these tasks 🚀]
• Design, build and operate pipelines to analyze the vast data generated by Quinit and PaldoGam.
• Load data generated from dozens of microservices in real-time into BigQuery via CDC, and improve streaming processing pipelines using Kafka and Debezium.
• Improve table exploration logic to increase the accuracy of AI-based data agents and build answer evaluation pipelines to monitor quality.
• Build a new Mart system that provides core business logic of the entire company in table format, and migrate the core Mart from the existing legacy system.
• Create governance where data producers also generate metadata, and build a Metadata platform to make it easier for company members to explore data.
• Establish a data quality management system by classifying table urgency, defining quality criteria, and establishing and operating an issue response process.
[Lap Labs is looking for those who 🚀]
• Have more than 5 years of experience in data engineering or equivalent experience and skills.
• Proficient in SQL and can skillfully handle at least one programming language such as Python.
• Have experience in building and operating cloud-based data infrastructure such as BigQuery and Airflow.
• Have built large-scale data pipelines using open-source data processing systems like Kafka and Spark.
• Have introduced and operated AI agents or LLM-based functions in actual services or internal systems.
[Experience in the following areas is a plus! 🚀]
• Have built and managed data infrastructure in a Kubernetes environment.
• Have managed data quality using Dataform or built data catalogs.
• Have built real-time data pipelines using CDC (Change Data Capture).
• Have experience scaling data infrastructure in fast-growing commerce or platform environments.
[The journey to joining Lap Labs 🚀]
• Application process: Document screening ＞ 1st Practical Interview > 2nd Culture Interview > Negotiation of terms > Final Acceptance
◦ The process may change or be added with prior notice depending on the schedule and situation.
◦ Regardless of the results of each screening (pass/fail), all applicants will be contacted individually within 1-2 weeks.
◦ For full-time positions, a 3-month probation period applies. During this period, 100% of the salary will be paid, and the probation may be extended or terminated based on evaluation.
◦ If any false information or discrepancies are found in the submitted resumes or supporting documents, acceptance may be canceled.
[You can grow in the data engineering team 🚀]
• Evolve into a data platform suitable for the AI era. You will directly design and build from AI-based data agents to metadata platforms.
• You will have the experience of redesigning data architecture from scratch in the process of eliminating legacy infrastructure and transitioning to new systems.
• You can establish the data governance of a rapidly growing commerce platform and create a company-wide data culture.
• From data quality management to real-time streaming, you will experience the entire spectrum of data engineering and grow into a senior engineer.
[Words from a colleague you will work with 🚀]
Our team is currently in the process of completely rebuilding the data platform. We are building AI-based data agents, eliminating legacy systems, and simultaneously creating new Mart systems and metadata platforms. In this environment, there are no predetermined answers, and we must quickly validate and boldly discard to find what truly makes an impact. Therefore, we are looking for someone who wants to define the direction of the data platform together, not just maintain the given pipeline. Let's dig deep into what 'good data' means in the AI era and actually create it together.
