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← SPARTAN X TECHNOLOGY

Data Engineer Mid-Senior

SPARTAN X TECHNOLOGY · Hồ Chí Minh
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Loại hình
Toàn thời gian
Hình thức
Tại văn phòng
Cấp bậc
Nhân viên
Ngành nghề
Kỹ thuật / Cơ khí
Mức lương
Thương lượng
Địa điểm
Thành phố Thủ Đức, Hồ Chí Minh, Hồ Chí Minh

Tổng quan

  • Build ingestion for operational, telemetry, and business data: ETL and ELT, incremental loads, CDC, backfills that don't take the system down.
  • Model data for how it gets used: event schemas upstream, star schemas and marts downstream.
  • Build streaming pipelines that land data in near real time: windowed aggregations, stream joins, late and duplicate events, exactly-once writes to the sink.
  • Own data infrastructure end to end: ingestion, processing, storage, serving.
  • Own data quality: contracts, schema changes, lineage, and alerts that fire before a founder spots a wrong number.
  • Build the data layer behind AI features: feature pipelines, embeddings and vector stores, RAG source data, eval datasets.
  • Build the serving layer: APIs and services that expose metrics, aggregates, and feature data to product teams.
  • Ship fast with a proven process: async standups, PRDs/RFCs, code reviews, and an AI-native workflow on Cursor, Claude, and Codex.
  • Talk directly with US startup founders and product leaders. Your input shapes what gets measured and what gets built.
  • Wear more than one hat. Data first, but you'll touch backend, infra, and AI integration when the project needs it.
  • Help shape Spartan's engineering culture. We work like founders, not contractors.
  • Your Skills and Experience
  • Fluent English. You'll talk directly with US startup founders.
  • 3+ years building and running production data pipelines: ingestion, ETL and ELT, incremental loads, backfills, and a plan for when a run fails halfway through.
  • Data modeling you can defend: normalized schemas for OLTP, dimensional models for analytics (star schema, fact and dimension tables, slowly changing dimensions), and a reason for when you denormalize instead.
  • Strong SQL. You can read a query plan and know why the job got slow.
  • You've run pipelines on an orchestrator in production: Airflow, Dagster, Prefect, or similar. Scheduling, dependencies, retries, idempotent reruns.
  • Strong coding skills in Python, Java, Kotlin, or similar. You write tested, reviewable code, not one-off scripts.
  • Hands-on with streaming data in production: Kafka, Pulsar, Kinesis, or similar. You've dealt with consumer lag, replays, and duplicate events.
  • Comfortable in the cloud. You deploy and debug your own pipelines on AWS, and Terraform or a Kubernetes manifest doesn't scare you.
  • Strong CS fundamentals: distributed systems, partitioning, and what breaks when the data outgrows one machine.
  • You use AI tools like a pro: you review what they write, catch their mistakes, and know when not to use them.
  • A builder's mindset. You don't just move data; you build platforms that make data useful.
  • Nice to Have
  • Stream processing in production: Flink, Spark, or Kafka Streams.
  • Warehouse and lakehouse work: dbt, Iceberg or Delta, or a real-time OLAP store like ClickHouse or Druid.
  • Durable execution for long-running workflows: Temporal or similar.
  • IoT or telemetry data at scale: high-volume ingest, late and out-of-order events, device fleets.
  • ML or LLM infrastructure: feature stores, vector databases, training or eval data pipelines.
  • You've built an internal data platform from scratch.
  • Domain experience in rideshare, logistics, mobility, transportation, or another data-heavy vertical.
  • Tech We Use
  • Every project brings a different stack, so in a year here you'll touch more of this list than most engineers do in five. What we adopt is decided by our engineers through an internal Tech Radar, not handed down from a slide deck.
  • Languages: Kotlin/Java · Python · SQL
  • Streaming: Kafka · Pulsar · AWS Kinesis · SQS
  • Processing: Flink · Spark
  • Orchestration: Airflow · Temporal (durable execution)
  • Storage: PostgreSQL · Aurora · Redis · S3
  • Warehouse & analytics: Redshift · BigQuery · ClickHouse
  • Data serving: REST APIs · event-driven architecture

Quyền lợi

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