[About the Platform Department]
The Platform Department brings together members from Data Engineering, SRE/DevOps, and MLOps to carry out the development and operation of the central platform and system infrastructure. We provide infrastructure and common platform technology to all company services, including Azar and AI/ML, and create business impact across a range of areas through active collaboration with related departments. We also focus on preventing silos across the company's technology organization and building an efficient, highly productive engineering culture.
[Data Analytics Engineering Team]
- The Data Analytics Engineering team is an engineering team responsible for Data Governance, Analytics Engineering, and BI/DW engineering.
- Beyond simply managing data pipelines, we build and provide sustainable data pipelines and data models that create valuable data and in which SLO/SLIs for Data Quality are managed.
- In developing and managing DataLake/Warehouse, we adopt modern data stack paradigms such as Data Governance and Analytics Engineering, and apply software engineering practices such as version control, testing, deployment, monitoring, and observability to the Data Platform.
- Data (as a) Product: We treat and develop data as a software product. We also serve as a bridge between data producers and data consumers, and we are interested in shifting data producers right and data consumers left.
- To raise the organization's data literacy and improve understanding and use of data models, we create and manage data documentation and metadata.
- We actively contribute to the development of internal and external data products for monetization, statistics, and operations built on aggregated data models.
[If you join the team]
- You can take ownership of managing data from large-scale global environments arising across diverse domains.
- Beyond maintaining data pipelines, you can design and build systems that solve business problems based on data.
- You can gain experience developing Data Applications needed by the business.
- Because we handle global data, the scale is very large (tens of TB/day or more), and you can tackle a wide range of technical challenges and experiments with large volumes of data.
- You can continuously research better directions than the status quo and rationally apply new work systems or systems to production.
- We use EKS, BigQuery, Databricks, Airflow, and dbt, and you can gain experience with various public cloud-based data infrastructure and frameworks.
Responsibilities After Ledger, you will design and operate Silver+/Mart data products with Airflow + dbt, and take final responsibility for data integrity, reproducibility, and reliability. Build and manage the metrics and semantic layer (standardize metric definitions, calculation formulas, versioning, and change impact). Operate data quality and observability (dbt tests, custom checks, anomaly detection for key metrics, trust-grade model). Standardize analytics serving (dashboards, reports, KPI monitoring; collaborate with PA, PM, MKT, and FP&A). Contribute to experiment and analytics automation, Reverse ETL specifications, and data governance (schemas, terminology, ownership). Compliance engineering (privacy and DLP): define requirements for blocking at the collection stage, implement anonymization, pseudonymization, and aggregation processing, handle withdrawal events, perform regular batch deletion and anonymization, and apply DLP (data loss prevention) policies at the data layer.
Requirements Hands-on data pipeline engineering experience: SQL + Python, operating Airflow/dbt. Understanding of data warehouse modeling and performance (Databricks / BigQuery, etc.). Experience applying software engineering methodologies such as testing, CI/CD, and version control to data pipelines. Experience understanding business domains and turning data into a trustworthy product. Independent design and ownership, with cross-team communication and coordination skills.
Preferred Qualifications Experience directly building a semantic layer / MetricFlow or a data quality framework. Experience with large-scale traffic data and real-time or near-real-time processing. Experience building or operating data privacy and compliance engineering or DLP. Strength in at least one of: Product Analytics, Growth/Marketing, FP&A, or AI/ML.
Hiring Process Employment type: Full-time Hiring process: Document screening > Assignment > Recruiter call > 1st interview > 2nd interview > Final acceptance (*The process may be added to or changed as needed.) Required documents: Freely formatted resume in Korean or English (PDF) detailing career experience
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If false information is found in the submitted materials, or if there is a legal disqualification for providing labor, the hiring may be cancelled, and additional screening and document verification beyond the hiring process announced in advance may be conducted if necessary. Candidates who are national veterans' beneficiaries will be given preference under relevant laws; if applicable, please indicate this when applying and submit supporting documents upon hiring. For applications to positions hired by Hyperconnect, this privacy policy applies to the handling of personal information: https://career.hyperconnect.com/privacy
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