全职 Senior Data Engineer 工作, 薪水, BonusLink Selangor 公司招聘中 - Ricebowl

You'll be joining a lean data team spanning BI, Data Science, and Data Engineering — reporting directly to the Head of Data. You'll work alongside an existing Data Engineer (whom you'll mentor) and collaborate closely with an Analytics Engineer who owns the transformation and modelling layers above yours.

This is a lead role, not a support role. You'll be the most senior technical voice in Data Engineering, with direct influence over architecture decisions.

We're migrating to a Medallion architecture (Bronze → Silver → Gold) across AWS and GCP:

Our core stack: Dagster (orchestration) · dbt (transformation) · Athena + S3 (AWS query layer) · BigQuery (GCP warehouse) · Redash (BI serving)

What You'll Be Doing

• Design — Design and maintain scalable ELT pipelines ingesting 23M+ loyalty events monthly across AWS (Athena, S3, Glue, Lambda) and GCP (BigQuery, Pub/Sub).
• Lead orchestration — Build and operate production pipelines in Dagster, including scheduling, dependency management, retries, and observability. Migrate any legacy Airflow workflows.
• Drive platform reliability — Own pipeline monitoring, incident response, data quality enforcement, and SLA management across the ingestion layer.
• Set the engineering standard — Establish DataOps practices: CI/CD for pipelines, testing frameworks, alerting, and documentation. This becomes the baseline the whole team works to.
• Mentor the existing DE — Actively develop the junior Data Engineer through code reviews, pairing, and structured technical guidance.
• Be dbt-aware — You won't own the dbt models (that's the Analytics Engineer), but you need to understand how your pipelines feed them, contribute to Bronze-layer dbt sources, and collaborate on data contracts between layers.
• Optimise infrastructure — Tune query performance on Athena and BigQuery, manage storage costs, and improve throughput at scale.
• Collaborate cross-functionally — Work with Analytics Engineering, Data Science, and product teams to translate requirements into reliable data foundations.

What We're Looking For

Must-Have

• 5+ years of hands-on data engineering experience, with at least 2 years in a senior or lead capacity
• Strong Python and advanced SQL (CTEs, window functions, query optimisation)
• Production experience with Dagster or Airflow — Dagster strongly preferred; Airflow experience accepted with clear willingness to migrate
• Solid hands-on experience with AWS (Athena, S3, Glue, Lambda, SQS) and/or GCP (BigQuery, Dataflow, Pub/Sub)
• Proven track record building and maintaining ELT pipelines at scale (millions of events/day)
• Working knowledge of dbt — you don't need to be the modeller, but you must understand how pipelines feed transformation layers and be able to contribute to source definitions
• Strong grasp of data modelling — star/snowflake schemas, dimensional design, Medallion layer separation
• DataOps fundamentals — Git, CI/CD, pipeline monitoring, alerting, and observability
• Demonstrated ability to mentor junior engineers and establish team-wide engineering standards

Nice-to-Have

• Real-time / streaming experience (Kafka, Kinesis, Pub/Sub)
• Containerisation (Docker / Kubernetes)
• Experience in loyalty, fintech, or high-transaction-volume domains
• Familiarity with Redash or similar BI query tools
• Exposure to data governance, data contracts, or schema registries

Qualifications

• Bachelor's in Computer Science, Engineering, or related field (or equivalent experience)
• 5+ years in data engineering, with at least 2 years in a senior or lead capacity