[Platform Department Introduction]
The Platform Department brings together Data Engineering, SRE/DevOps, and MLOps professionals to develop and operate central platforms and system infrastructure. We provide infrastructure and common platform technologies to all company services including Azar and AI/ML, and create business impact across diverse areas through active collaboration with related departments. We are also focused on preventing silos in the company's technical organization and building an efficient, highly productive engineering culture.
[Data Analytics Engineering Team]
- The Data Analytics Engineering team is an engineering team that performs Data Governance, Analytics Engineering, and BI/DW engineering.
- Beyond simple data pipeline management, we develop and provide sustainable data pipelines and data models that create valuable data and manage SLO/SLI for data quality.
- In managing and developing DataLake/Warehouse, we adopt modern datastack technology paradigms such as Data Governance and Analytics Engineering, and apply software engineering practices including version control, testing, deployment, monitoring, and observability to our 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 are interested in shifting data producers right and data consumers left.
- We create and manage data documentation and metadata to increase the organization's data literacy and enhance understanding and utilization of data models.
- We actively contribute to the development of internal and external data products for monetization, statistics, and operations based on aggregated data models.
[When You Join the Team]
- You will be able to proactively manage large-scale global data generated across diverse domain environments.
- Beyond maintaining data pipelines, you can design and build systems to solve business problems based on data.
- You will gain experience developing data applications needed for the business.
- Since we handle global data at a very large scale (tens of TB/day and beyond), you can explore diverse technical challenges and attempts based on massive amounts of data.
- You can continuously research better approaches and rationally apply new work systems or systems to production.
- We currently use EKS, Bigquery, Databricks, Airflow, and DBT, and you can experience various data infrastructure and frameworks based on public cloud.
Responsibilities
- Design and operate Silver+/Mart data products after Ledger using Airflow + dbt, and take final responsibility for consistency, reproducibility, and reliability.
- Build and manage metric and semantic layers (standardize metric definitions, formulas, versions, and change impact).
- Operate data quality and observability (dbt tests, custom checks, anomaly detection for key metrics, trust grade models).
- Standardize analytics serving (dashboards, reports, KPI monitoring, collaboration with PA, PM, MKT, FP&A).
- Contribute to experiment and analysis automation, Reverse ETL specifications, and data governance (schema, terminology, ownership).
- Compliance engineering (privacy, DLP): define blocking requirements at collection stage, implement anonymization/pseudonymization/aggregation processing, handle deletion events, perform regular batch deletion and anonymization, apply DLP (data loss prevention) policies at the data layer.
Requirements
- Hands-on experience with data pipeline engineering — SQL + Python, Airflow/dbt operations
- 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 converting data into trustworthy products
- Ability for independent design and ownership, and cross-team communication and coordination
Preferred Qualifications
- Direct experience building semantic layers / MetricFlow and data quality frameworks
- Experience with large-scale traffic data and real-time/near-real-time processing
- Experience with data privacy, compliance engineering, or DLP implementation and operations
- Strength in one or more 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 Offer (*Additional stages may be added or modified as needed.)
- Document screening results will be communicated individually to successful candidates.
- Application Materials: Detailed career-based resume in Korean or English (PDF) in any format
If any false information is found in the submitted materials or if there are grounds for disqualification under applicable laws, the hiring may be cancelled. If necessary, additional screening and document verification may be conducted beyond the hiring process previously announced.
Veterans are given preferential treatment in accordance with applicable laws. If you are eligible, please notify us at the time of application and submit supporting documents upon hiring.
When applying for positions hired by Hyperconnect, this privacy policy applies to the processing of personal information: https://career.hyperconnect.com/privacy
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