Data Engineer

A Global IT Service firm is looking for a Data Engineer for its AI & Data Division. This division is responsible for the AI products such as chatbots, voicebots, and copilot tools to support human operators.
You'll join a 60-person AI for Customer Service team of engineers, scientists, data analysts, and dialogue designers, responsible for modernizing legacy contact-center tooling and workloads into an agentic Generative AI stack. This role is responsible for the entire data backbone of a V1 product: pipelines, storage, and the data platform that our analysts and product managers will depend on from day one.

Responsibilities:
- Design, build, and own the data infrastructure for the V1 CCaaS product, from ingestion through serving, supporting the business unit's analytics needs
- Develop and operate production-grade ETL/ELT pipelines with orchestration tools, ingesting and transforming the large volumes of data generated by chatbot events and customer journey interactions
- Architect and optimize cloud data warehouse datasets across medallion-style layers (raw bronze, cleansed silver, aggregated gold), with partitioning, clustering, and SQL tuning for large-scale analytical workloads
- Design event-centric data models at session and turn grain that serve both operational features and analytical reporting, keeping the product's operational layer and analytical layer consistent
- Keep every pipeline reliably dependable in production through data quality checks, monitoring, and observability, so analysts and product managers can trust the numbers they see on dashboards
- Partner with Data Analysts and Product Managers to understand data needs. Requests typically start as one-off extractions and are iterated into scalable, reusable data products as demand grows or as they feed into the main pipeline
- Integrate real-time or near-real-time data streams as the product's event volume grows
- Establish data governance fundamentals: standardized event definitions, schemas, and naming conventions across all smart agents so their data stays comparable and combinable, plus documentation, lineage, and access control appropriate for an early-stage, fast-moving team

Required skills:
- Over 5 years of hands-on experience in data engineering, including building and operating production data pipelines
- Advanced SQL and strong proficiency in at least one programming language such as Python or Go
- Experience with cloud data warehouses such as BigQuery, Snowflake, or Databricks, including performance tuning at scale
- Experience with ETL/ELT orchestration tools such as Apache Airflow, dbt, or Google Cloud Dataflow
- Experience designing analytics data models and data marts (e.g., dimensional modeling, dbt or similar) across layered architectures (medallion bronze/silver/gold), owning their quality and documentation
- Familiarity with streaming technologies such as Kafka or Pub/Sub for real-time ingestion
- Experience building data infrastructure from scratch (greenfield or V1 product context) rather than only maintaining existing platforms
- Experience partnering with analysts, product managers, or engineers to deliver reliable, reusable data solutions
- Ability to design data solutions robust enough to absorb the ambiguity and changing requirements typical of V1 product development in a fast-paced, agile environment

Why should you apply:
- Great opportunity to be able to work for well-established company
- Cross-departmental communication is possible in an international environment


Company Details:
Our client is a Global company with a strong presence across several business divisions. It has enjoyed sustained growth both domestically and overseas, including US and Europe. It proud itself of a diverse and international environment and a strong focus on equal opportunities, which translates into plenty of career opportunities. Given the variety of businesses, it deals with a strong variety of technologies so it is a great place to expand your skills!
Also, employees are welcome to choose their own setup (Windows/Mac, etc.); whatever makes them comfortable! Free meals are also provided at the company cafeteria. Their chefs work to create exciting new menus and dishes, so employees never get tired of the food!

Work Hours: 9:00 AM – 5:30 PM (Flextime or staggered working hours possible)
Work Style: Hybrid (Typically 4 days in the office, 1 day working from home)
Holidays: Saturdays, Sundays, public holidays, New Year holidays, paid leave, bereavement leave, and other special leave
Benefits: Full social insurance (employee pension insurance, health insurance, worker’s accident compensation insurance, unemployment insurance), DC pension plan, transportation allowan