Data Platform Engineer: Lakehouse & MPP Expert

Tap Growth ai; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

Tap Growth ai is recruiting a Data Platform Engineer to design, build, and optimize modern analytics platforms and lakehouse solutions. You will develop scalable data pipelines and optimize distributed query workloads across a high-performance data ecosystem.

The role requires deep expertise in data architectures, MPP platforms, and large-scale analytics processing, with collaboration across architects, analysts and engineers to ensure reliable and scalable data solutions.

Qualifications

• Bachelor's degree in CS/IT/Data Engineering or related field.
• 7-8+ years in Data Engineering, Analytics Engineering, or Big Data ecosystems.
• Experience in enterprise-scale analytics environments.
• Strong analytical, problem solving and stakeholder management skills.

Responsibilities

• Design, develop and optimize enterprise data platforms and analytics solutions.
• Build scalable data pipelines and lakehouse architectures.
• Support and optimize MPP query engines, data warehouses and distributed environments.
• Drive performance tuning, platform modernization and workload optimization.
• Collaborate with architects, analysts and engineering teams to deliver scalable data solutions.
• Support platform automation, CI/CD, governance and operational best practices.
• Troubleshoot complex data platform issues and ensure operational excellence.

Skills

Analytical thinking

Problem solving

Stakeholder management

Communication

Education

Bachelor's Degree in Computer Science, Information Technology, Data Engineering, or a related discipline

Tools

Apache Doris

StarRocks

ClickHouse

Greenplum

Iceberg

Delta Lake

Apache Hudi

Snowflake

Apache Spark

Trino/PrestoSQL

Presto

Parquet

ORC

Python

Java

Scala

Git

CI/CD pipelines

Job description

Tap Growth ai is recruiting a Data Platform Engineer to design, build, and optimize modern analytics platforms and lakehouse solutions. You will develop scalable data pipelines and optimize distributed query workloads across a high-performance data ecosystem.

The role requires deep expertise in data architectures, MPP platforms, and large-scale analytics processing, with collaboration across architects, analysts and engineers to ensure reliable and scalable data solutions.