Editorial summary

The company is seeking a Data Engineer - Databricks to design, build, and support reliable data ingestion and transformation pipelines on AWS and Databricks. The role collaborates with architects, engineers, QA, and business teams to deliver data pipelines, curated datasets, data-quality controls, and production-ready operational processes. Key requirements include designing batch and streaming pipelines using Databricks and AWS, processing data across Bronze, Silver, and Gold layers with SQL, Python/PySpark, and Delta Lake, and enabling ingestion from APIs, files, databases, and object storage. Compensation is 8,200.

This summary is AI-generated and may contain inaccuracies. Please refer to the full job description below.

Job description

[This job https://singapore.job-q.com/jobs/detail/data-engineer-databricks-37449 first appeared in Job-Q.com on 12 Oct 2026]

The Data Engineer will design, build, and support reliable data ingestion and transformation pipelines on AWS and Databricks. The role will work with architects, engineers, QA, and business teams to deliver data pipelines, curated datasets, data-quality controls, and production-ready operational processes.

Responsibilities:

- Design and build batch and/or streaming data pipelines using Databricks and AWS services.

- Develop data processing across Bronze, Silver, and Gold layers using SQL, Python / PySpark and Delta Lake.

- Implement ingestion from APIs, files, databases, object storage, or other enterprise data sources.

- Develop data transformations, dimensional or curated datasets, and reusable data-engineering components.

- Implement data validation, reconciliation, error handling, logging, rerun/recovery and monitoring.

- Optimize Spark jobs, clusters, partitioning, and storage for performance and cost.

- Support CI/CD, environment promotion, SIT/UAT, production deployment, documentation, and operational handover.

- Collaborate with platform, application, BI, and data-governance teams.

Mandatory Requirements:

- 3+ years of experience in data engineering or enterprise data-platform delivery.

- Hands-on experience with Databricks, Apache Spark, PySpark, and SQL.

- Experience with Delta Lake and layered data architecture such as Bronze/Silver/Gold.

- Experience building production data pipelines with proper logging, error handling, and recovery.

- Experience with AWS services such as S3, IAM, Glue, Lambda, Kinesis, or related services.

- Good understanding of data modelling, data quality, and performance optimization.

Plus Points (Advantage)

- Databricks Workflows, Delta Live Tables, Unity Catalog, Auto Loader, or Structured Streaming.

- Experience with APIs, XML/JSON, event or near-real-time ingestion.

- CI/CD and infrastructure/configuration automation using Git-based tools.

- Experience with Tableau, BI datasets, or data warehouse integration.

- Databricks, AWS, data engineering, AI/ML, or GenAI-related certification(s).

Interested candidates may send their CV to MAC (Reg No. R1221300) quoting the job title in the Subject line. We regret that only shortlisted candidates will be notified.

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