Introduction to Platform Department

Hyperconnect Infra Department consists of members from Data Engineering, SRE/DevOps/MLOps, Platform, and IT, performing central platform and system infrastructure development and operation roles. We provide infrastructure and common platform technologies to all company services, including Azar and AI/ML, and actively collaborate with related departments to create business impact in various areas. We also focus on preventing siloization of the company's technical organizations and creating an efficient and highly productive engineering culture.

Data Product Dev Team

• The Data Product Dev Team is an engineering team of Data Engineers who perform Data Governance, Analytics Engineering, and BI/DW Engineering.
• Beyond simple data pipeline management, we create valuable data and develop and provide sustainable data pipelines and data models where SLO/SLI for Data Quality are managed.
• In DataLake/Warehouse management and development, we introduce Modern Datastack technology paradigms such as Data Governance and Analytics Engineering, and apply Software Engineering practices like version control, Testing, Deploy, Monitoring, and Observability to the Data Platform.
• Data (as a) Product: We treat and develop data as a software product. We also act as a bridge between data producers and data consumers, interested in shifting right data producers and shifting left data consumers.
• We increase the organization's Data Literacy and create and manage data documentation and metadata to enhance understanding and utilization of data models.
• Actively contribute to the development of internal and external data products for monetization/statistics/operations based on aggregated data models.

If you join the team

• You can proactively manage data generated in diverse domain environments based on multi-products.
• Beyond data pipeline maintenance, you can build and design systems to solve business problems based on data.
• If necessary, you can gain experience developing Data Applications required for business.
• Dealing with global data means the scale is very large (tens of TB/day or more), allowing for various technical considerations and attempts based on large amounts of data.
• You can continuously research better directions and reasonably apply new work systems or system introductions to Production.
• We utilize EKS, Bigquery, Databricks, and Airflow, allowing you to experience various data infrastructures and frameworks based on public clouds.

Check out how the Data Engineering team works and what problems they solve through the Tech Blog and Interview X Hacker!

• Interview x Hacker Data Engineering
• Bigquery Storage Optimization Progress

Work Content

Data Architect (Analytics)

• Implement Data Vault modeling and introduce and build streaming/batch data processing systems based on data modeling.
• Collect needs from various stakeholders to define company-wide data standards.
• Develop end-to-end data analysis architecture, including data integration, data warehousing, data modeling, data visualization, and reporting.
• Design and optimize data models to support efficient data storage, retrieval, and analysis, ensuring data integrity, accuracy, and reliability.
• Improve the data environment to enable optimal decision-making by bridging business and data.
• Transform complex data into understandable insights and actively achieve new business opportunities.

Data Quality Management

• Automate verification of data consistency, timeliness, and errors.
• Create and operate a single data source for data quality operational management.

BI Operation, Design & Implementation

• Collaborate with various organizations across the company to establish policies for BI operations.
• Analyze data user/analyst requirements to design and implement dashboards.

Data Analysis Engineering

• Collaborate with data science/analysts to perform BI and analysis engineering tasks, enabling fast EDA and data analysis.
• Build and expand integrated data modeling for data democratization.
• Produce and manage Hyper Data (Data Catalog and Data Knowledge).
• Establish policies and guide data user self-service activation.

Data Application Development

• Produce streaming tables that can be utilized by business and backend/ML.
• Beyond analysis, develop Data Applications using Datalake-based data to improve monetization, operations, and engagement efficiency.
• Develop Data Applications for privacy and security-related data processing and provision.
• Generate and provide near real-time data dashboards and aggregated data.
• Utilize and develop ReverseETL tools based on the produced data mart models to link data with various third-party systems.

Requirements

• Over 6 years of professional data work experience, with at least 3 years of Data Analytics Engineering experience.
• Ability to concisely and systematically implement analysis using SQL.
• Basic CS knowledge and proficient Python skills.
• Proficiency in BI tools such as Tableau, Power BI, Looker, or similar.
• Familiarity with leading data governance standards across organizations, integrated operations, and data quality management.
• Strong problem-solving skills and the ability to design scalable and efficient data analysis architectures.
• Experience proactively leading DW/DM data modeling, pipeline construction, and operation tasks.
• Ability to quickly grasp business and diverse domain knowledge, and proactively solve data problems.
• Proficiency in Airflow (ETL) and experience collaborating with various stakeholders to lead and resolve troubleshooting.
• Ability to communicate in basic conversational English.
• Practical experience using public cloud platforms like AWS, Google Cloud.

Preferred Qualifications

• Proven track record of excellent performance in building data governance and data platforms.
• Experience leading and optimizing effective BI governance across large-scale data and diverse organizations.
• Achieved results using tools like dbt to improve data quality management and pipeline efficiency.
• Enjoys quickly understanding new business domain knowledge and contributing to data-driven decision-making.

How We Work

• You can define undefined tasks and find solutions independently.
• You can successfully lead team projects, drive inter-team collaboration, and collaborate with partners from other functional roles.
• You can communicate clearly and concisely, tailoring your message to the audience both within and outside the team.
• You support inter-team collaboration and help achieve results.
• You can align project outcomes with team goals.

Employment Type/Hiring Process/Working Hours

• Employment Type: Full-time
• Hiring Process: Document Screening > Technical Competency Test > Recruiter Call > 1st Interview > 2nd Interview > 3rd Interview (if applicable) > Final Offer (*Additional interview stages may be conducted if necessary.)
• For the document screening, only successful candidates will be notified individually.
• Application Documents: Free-form detailed resume in Korean or English (PDF)
• This position is open for Professional Research Personnel (현역 편입/전직, 보충역 편입/전직). For military service exceptions, service management will proceed according to relevant military service laws.

If there is any false information in the submitted content or if there are disqualifying reasons for employment under relevant laws, the employment may be canceled. If necessary, additional screening and document verification may be conducted beyond the pre-announced hiring process.

National meritorious persons are given preferential treatment according to relevant laws; please notify us when applying and submit supporting documents upon employment.

When applying for a position at Hyperconnect, this privacy policy applies to the processing of personal information: https://career.hyperconnect.com/privacy

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