Big Data Analyst
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Summary
Data analyst on Kompas Gramedia's internal team, doing quantitative and textual analysis, data-quality monitoring, and reporting/dashboards for internal stakeholders, sub-companies, and third parties. Core stack: SQL (PostgreSQL/MySQL), Python or R, BI tools like Tableau/Power BI, with AWS data services and LLM tooling as pluses.
- Investigate questions that span qualitative textual analysis and quantitative analysis. Identify trends, isolate anomalies, and drive to the root cause rather than stopping at the first correlation.
- Treat data quality as a named deliverable: freshness and volume checks, schema-change alerting, and lineage that makes it possible to trace any number in any deck back to its source.
- Maintain data documentation and dictionaries that stay current as the stack changes.
- Develop and present reports, charts, graphs, and tables for internal stakeholders, sub-companies, and third parties.
Minimum Qualifications:
- 2+ years of industry experience in data handling, reporting, and analysis.
- BS or higher in data analytics, statistics, mathematics, or a related technical field or equivalent practical experience.
Core Technical Skills
- Strong quantitative and analytical skills, with sound judgment about statistical significance, sample size, and confounding.
- Strong SQL (PostgreSQL, MySQL or similar): joins, window functions, CTEs, and query performance on large tables.
- Python or R with the relevant analytical libraries (e.g. pandas, numpy, statsmodels, scikit-learn, or the tidyverse equivalents).
- Experience with BI or analytical software such as QuickSight, Tableau, Power BI, Looker, or Metabase.
- Practical fluency with AI tooling in the analytics workflow, using LLMs and embeddings for text analysis, code assistance, and exploratory work, alongside a clear-eyed view of where these tools fail and how to catch it.
Nice to Have
- AWS data workflow : Lake Formation, Glue, Crawler, Data Catalog, and Athena.
- Experience with lakehouse table formats (Iceberg, Delta, or Hudi) and layered data architectures (raw, transformed, curated).
- Working knowledge of GA4, server-side tracking, and multi-touch or cross-platform attribution, including its limits.
- Understanding of how AI-driven discovery is reshaping publisher traffic AI summaries, zero-click search, referrals from conversational assistants, and how to measure it.
