Senior Data & Analytics Engineer

[About the Platform Department]

The Platform Department consists of members from Data Engineering, SRE/DevOps, and MLOps, who are responsible for the development and operation of our central platform and system infrastructure. We provide infrastructure and common platform technologies for all company-wide services, including Azar and AI/ML, and create business impact across various areas through active collaboration with relevant departments. We are also focused on preventing silos within 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 handles Data Governance, Analytics Engineering, and BI/DW engineering.
• Beyond simple data pipeline management, we develop and provide sustainable data pipelines and data models where valuable data is created and SLO/SLI for Data Quality are managed.
• In managing and developing our Data Lake/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 act as a bridge between data producers and data consumers, with an interest in shifting right for data producers and shifting left for data consumers.
• We create and manage data documentation and metadata to increase the organization's data literacy and improve the 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.

[If you join our team]

• You can proactively manage data in a large-scale global environment across various domain environments.
• Beyond maintaining data pipelines, you can design and build systems to solve business problems based on data.
• You will have the experience of developing data applications necessary for the business.
• As we handle global data, the scale is massive (over tens of TB/day), allowing you to explore and attempt various technical solutions based on large volumes of data.
• You can constantly research better directions and reasonably apply new work systems or system introductions to production.
• We utilize EKS, BigQuery, Databricks, Airflow, and dbt, and you will be able to experience various data infrastructures and frameworks based on public cloud.

Responsibilities

• Design and operate Silver+/Mart data products after the Ledger stage using Airflow + dbt, and take final responsibility for consistency, reproducibility, and reliability.
• Build and manage metrics/semantic layers (standardizing metric definitions, calculation formulas, versions, and change impacts).
• Operate data quality and observability (dbt tests/custom checks, anomaly detection for core metrics, trust rating models).
• Standardize analysis serving (collaborating with PA, PM, MKT, and FP&A on dashboards, reports, and KPI monitoring).
• Contribute to experiment/analysis automation, Reverse ETL specs, and data governance (schemas, terminology, ownership).
• Compliance engineering (privacy/DLP): define requirements for blocking at the collection stage, implement anonymization/pseudonymization/aggregation, handle withdrawal events, manage periodic batch deletion/anonymization, and apply DLP (Data Loss Prevention) policies to the data layer.

Requirements

• Practical experience in data pipeline engineering — SQL + Python, Airflow/dbt operation.
• 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 transforming data into reliable products.
• Ability to perform independent design, take ownership, and engage in cross-team communication and coordination.

Preferred Qualifications

• Experience building a semantic layer / MetricFlow or data quality frameworks from scratch.
• Experience with large-scale traffic data and real-time/near-real-time processing.
• Experience in data privacy/compliance engineering or building/operating DLP.
• Strengths in at least one of the following: Product Analytics / Growth/Marketing / FP&A / AI/ML.

Hiring Process

• Employment Type: Full-time
• Hiring Process: Document Screening > Assignment > Recruiter Call > 1st Interview > 2nd Interview > Final Offer (*The process may be added/changed if necessary.)
• Application Documents: Detailed career-based resume in Korean or English (PDF) in free format.

If any false information is found in your submission or if there are grounds for disqualification under relevant laws, the offer may be rescinded. Additional screenings or document verifications may be conducted if necessary beyond the pre-announced process. National veterans are given preference in accordance with relevant laws; please notify us upon application and submit supporting documents if hired. When applying for a position at Hyperconnect, this Privacy Policy applies regarding the processing of personal information:

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