We are looking for a versatile Senior Data Engineer who combines strong technical engineering capabilities with a Data Analyst mindset. In this hybrid role, you will do much more than build backend data pipelines - you will directly transform raw data into reliable business assets, build interactive dashboards for decision-makers, maintain strict Data Quality standards, and actively monitor Machine Learning models running in production. If you enjoy working across the full data lifecycle - from raw ingestion to front-end visualization and ML performance tracking - this role offers a high-impact position within our data team.

Responsibilities:

Data Engineering & Pipeline Management

Design, construct, and maintain automated, scalable ETL/ELT data pipelines from diverse sources (SQL, NoSQL, APIs, Event Streams);
Architect and optimize Data Warehouse schemas or high-performance querying;

Analytics & Dashboarding (DA Focus)

Partner with business and product teams to translate commercial requirements into clean, user-friendly Dashboards & Reports;
Build, publish, and maintain BI reports using tools like Superset, Power BI, Tableau;
Perform ad-hoc data analysis to help business leaders answer critical strategic questions;

Data Quality & Governance

Implement automated Data Quality validation frameworks to monitor accuracy, completeness, consistency, and data freshness;
Set up real-time alerts for schema changes, pipeline failures, or abnormal data anomalies to minimize data downtime;

ML Performance & Drift Monitoring

Build telemetry pipelines to capture model predictions alongside real-world ground-truth outcomes;
Monitor key ML metrics (Accuracy, Precision, Recall, AUC, Latency ...) and set up alerts for Data Drift and Concept Drift;
Collaborate with Data Scientists/ML Engineers to flag performance degradation and trigger automated retraining loops.

Core Qualifications

3+ years of hands-on experience in Data Engineering, Analytics Engineering, or a hybrid Data Analyst/Engineer role;
Strong Programming & Querying: Advanced SQL (complex joins, CTEs, window functions, optimization) and Python (Pandas, PySpark, SQLAlchemy);
Data Warehousing & Orchestration: Direct experience with Onpremise Data Warehouses and orchestrators (Airflow, dbt, Prefect);
BI & Data Visualization: Demonstrated experience crafting executive-ready dashboards in Superset, Power BI, Tableau;
Quality & ML Monitoring Awareness: Familiarity with data testing tools (Great Expectations, dbt tests) and basic knowledge of Machine Learning evaluation metrics and monitoring concepts.

Soft Skills & Mindset

Business-to-Tech Translator: Ability to explain complex data architecture concepts to non-technical business partners clearly;
Data Ownership: High attention to detail with zero tolerance for silent data corruption or unmonitored failures;
Problem-Solving Drive: A proactive attitude toward identifying process bottlenecks and automating repetitive analytics tasks.

Senior Analytics Engineer, Zalo