Data Quality Engineer SIRCLO Penuh waktu Hybrid • Tangerang Selatan Min. 1 years of experience [...]

Define and execute data quality test strategies for batch and (if applicable) near-real-time data pipelines.
Build and maintain automated data quality checks across medallion layers (Bronze/Silver/Gold), including schema validation, completeness, uniqueness, timeliness, referential integrity, reconciliation, anomaly detection, business‑rule validations and KPI consistency checks.
Collaborate with engineering teams to implement QA gates in CI/CD for analytics code (e.g., dbt runs/tests).
Investigate data incidents and root causes (e.g., upstream source changes, transformation logic issues), and drive prevention actions.
Improve data observability practices: alerting, dashboards, SLAs/SLOs for data freshness and correctness.
Document and validate data lineage (source → transformation → reporting) and ensure traceability of key metrics.
Partner with stakeholders to define “data contracts” / acceptance criteria for datasets and reports.
Support governance activities: dataset certification, change management, and audit readiness.

3+ years in Data QA, Data Engineering QA, Analytics Engineering QA, or similar roles.
Hands‑on experience with dbt (models, tests, docs, exposures, sources; test strategy).
Strong SQL skills and experience working with BigQuery, relational DBs like PostgreSQL and MySQL, NoSQL systems (any common type: document, key‑value, wide‑column, etc.).
Experience validating datasets in a medallion pattern (Bronze/Silver/Gold) and understanding quality expectations at each layer.
Understanding of data lineage concepts (end‑to‑end traceability, impact analysis, upstream/downstream dependencies).
Ability to communicate findings clearly and collaborate across Data/Engineering/Product/Business teams.

Experience with BI/analytics tools such as Apache Superset (or similar: Looker, Metabase, Tableau, Power BI).
Familiarity with data quality/observability tooling (e.g., Great Expectations, Soda, Monte Carlo, Bigeye, Datadog, etc.).
Exposure to orchestration tools (e.g., Airflow) and CI/CD workflows for data pipelines.
Knowledge of dimensional modeling and metric definitions (semantic consistency across dashboards).
Experience with data governance practices (cataloging, documentation standards, data contracts).

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