Data Engineer

1) Data Platform & Pipeline for Search & Geocoding

• Build and operate pipelines to ingest/transform place, address and POI data from multiple sources (third-party, internal, user-contributed).

• Design data-processing flows for address/place enrichment, deduplication and normalization before indexing into search (OpenSearch/Elasticsearch).

• Build quality gates: anomaly detection, schema validation, and control of data freshness and coverage by market.

• Design CDC (Change Data Capture) and reindex pipelines to sync data zero-downtime with the search platform.

2) Traffic & Routing Data Pipeline

• Build stream-processing pipelines for real-time traffic: ingest probe data/GPS signals, aggregate per road segment, compute speed profiles, confidence scoring and freshness.

• Design batch pipelines computing historical traffic patterns, ETA baselines and speed bands per segment/time-of-day.

• Build a data serving layer (time-series/KV) optimized for low-latency queries by the routing engine.

• Manage road-network data: graph updates, schema versioning and delta updates for the routing engine.

3) Analytics & Reporting Platform

• Build a centralized data warehouse/lakehouse across all domains: search queries, routing requests, geofence events, map-ads impressions/clicks.

• Design data models (dimensional modeling / star schema) suitable for BI reporting and ad-hoc analytics.

• Build and maintain data marts for Map Ads reporting: impressions, clicks, frequency, budget burn and attribution pipelines.

• Automate data-quality checks and SLA monitoring on pipelines, with alerting on anomalies.

4) Geospatial Data Processing

• Build geospatial data-processing pipelines: tile processing, polygon simplification, geofence ingestion and S2/H3/Geohash indexing.

• Optimize spatial joins and point-in-polygon queries at scale: batching, spatial-index partitioning, caching.

• Support pipelines to create and update custom map styles, layer data and feature flags per tenant/market.

5) Feature Store & ML Data Serving

• Build a feature store for the team's ML models: ranking features for search, ETA correction, traffic anomaly detection.

• Design offline (batch) and online (low-latency) feature pipelines, ensuring training/serving consistency.

• Manage data lineage, feature versioning and audit trails.

• 3–5 years of Data Engineering experience; experience with large-scale, production-grade data systems preferred.

• Bachelor's degree (good grade or above) in IT, Electronics & Telecommunications, Applied Mathematics or equivalent (top universities preferred: HUST, University of Engineering and Technology, PTIT, University of Science).

• Proficient in Python and/or Scala/Java for data engineering; strong understanding of distributed data processing.

• Experience with distributed data-processing frameworks: Apache Spark, Flink or equivalent; solid grasp of batch and stream processing.

• Experience with Kafka/Kinesis or message-streaming platforms; building real-time ingestion pipelines.

• Experience designing data warehouse/lakehouse: data modeling, partitioning strategy, query optimization on Redshift/BigQuery/Snowflake or equivalent.

• Experience with workflow orchestration: Apache Airflow or equivalent; managing DAGs, retry logic and alerting.

• Advanced SQL experience: window functions, CTEs, query-plan optimization on large datasets.

• Data-quality ownership mindset: proactively building validation, monitoring and alerting for pipelines rather than waiting for downstream to catch errors.

Preferred

• Experience with geospatial data: PostGIS, GeoPandas, S2/H3/Geohash, spatial indexing.

• Mapping/mobility domain experience: road-network data, GPS-trace processing, map tiles, traffic data. Experience with dbt or equivalent for data transformation, lineage and documentation.

• Feature-store experience (Feast, Tecton or in-house): offline/online serving, versioning. Experience building data lakehouses: Delta Lake, Apache Iceberg, Hudi — ACID transactions, time travel, schema evolution. Experience with Kubernetes and cloud (EKS/GKE), containerizing data workloads, autoscaling.

• Understanding of ad-tech data: impression/click pipelines, attribution, budget reporting.

• Experience with BI tools (Superset, Metabase, Looker) or building reporting APIs. Experience with A/B testing data infrastructure and experimentation platforms.