Forward Deployed Data Engineer (Data & AI)

Salary: ฿40,000 – ฿60,000 per month

About the role

As a Forward Deployed Data Engineer (FDE), you will work directly with customers to design, build, deploy, and operate production-grade data pipelines, analytics products, and AI-ready data solutions. You will take ownership from business question to production outcome—staying accountable until the data is reliable, the numbers can be trusted, the solution is secure and supportable, and the customer's team understands how to operate and extend it.

Key responsibilities

• Build and operate production-grade data pipelines (batch and streaming) and analytics products, and prepare the data behind AI solutions such as RAG, enterprise search, and AI agents.

• Ingest data from enterprise sources, including ERP, CRM, SaaS platforms, APIs, databases, files, and legacy systems, into cloud data platforms such as BigQuery.

• Solve business questions, not just technical requirements. Start from the decision a stakeholder needs to make, define the metric behind it, and recommend the right data architecture.

• Design data models for analytics and AI, including dimensional models, medallion layers (bronze, silver, gold), and semantic or metric layers that give one consistent definition of each KPI.

• Build analytics products such as dashboards and self-service reports that stakeholders trust and use to make decisions.

• Design data quality checks and evaluation frameworks that measure reconciliation to source, freshness, accuracy, groundedness of AI answers, cost, and business outcomes before production deployment.

• Engineer for enterprise reality, including legacy systems, incomplete APIs, fragmented data, access restrictions, security boundaries, and compliance requirements.

• Own data solutions end-to-end across discovery, modeling, development, testing, deployment, monitoring, optimization, and knowledge transfer.

• Apply DevSecOps from day one, including CI/CD for data pipelines, Infrastructure as Code, IAM with least privilege, secrets management, PII protection, observability, and secure data access.

• Build reliable and cost-efficient data platforms with appropriate monitoring across storage, query cost, pipeline performance, data freshness, and AI consumption.

About you

• 2+ years of relevant data engineering experience, with experience in several of the following areas: data engineering (pipelines, ELT/ETL, orchestration), SQL and data modeling for analytics, cloud data platforms (BigQuery, Snowflake, Redshift, Synapse or Fabric, MaxCompute), analytics engineering (dbt, metric definitions, BI tools), generative AI and RAG (vector databases, embeddings, evaluation), enterprise security and IAM for data, production operations and troubleshooting.

• Experience with technologies such as Python, SQL, dbt, Airflow or Dagster, BigQuery, Snowflake, Spark, Kafka, Terraform, vector databases such as PgVector, LLMs, and agent frameworks is highly valuable.

• A degree in CS, software/data engineering, statistics, math, or a related field.

• Hands-on experience delivering enterprise data solutions with tested, idempotent, version-controlled pipelines and DataOps/DevSecOps practices (CI/CD, IaC, IAM, secrets management, observability, security-by-design).

• Knowledge of Landing Zone, cloud infrastructure, and billing, including BigQuery cost drivers.

• Uses AI to write, review, and test SQL and pipeline code, and knows how to verify its output.

• Questions and reconciles numbers before reporting them, and designs solutions that deliver measurable business value.

• Translates business needs into data requirements and presents confidently to technical and non-technical audiences.

• Works independently, learns fast, and owns production issues through resolution.