Data Engineer (Databricks) - Contract

ZENITH INFOTECH (S) PTE LTD.; Singapore; Presencial; SGD 70.000 - 110.000; Jornada completa

Descripción de la vacante

Zenith Infotech (S) PTE LTD. in Singapore seeks a Data Engineer to design, build, and maintain modern data pipelines using Databricks and Snowflake for analytics and ML readiness.

You will collaborate with analysts, data scientists, and product teams to turn raw data into trusted datasets, implement data quality checks, governance, and cost-efficient data platforms.

Formación

• 3 to 7 years of professional experience in data engineering.
• Strong hands-on experience with Databricks (Spark, PySpark/Spark SQL, Delta Lake, Workflows/Jobs).
• Strong hands-on experience with Snowflake (warehousing, SQL, Snowpipe, Streams & Tasks, performance tuning).
• Advanced SQL skills and proficiency in Python (Scala is a plus).
• Solid understanding of data warehousing, data modeling, and ETL/ELT design patterns.
• Experience with at least one major cloud platform (AWS, Azure, or GCP) and its storage and compute services.
• Experience with orchestration tools such as Airflow, Databricks Workflows, or Azure Data Factory.
• Familiarity with version control (Git) and CI/CD practices.
• Strong problem-solving, communication, and collaboration skills.

Responsabilidades

• Design, build, and optimize scalable batch and streaming data pipelines using Databricks (Spark, Delta Lake) and Snowflake.
• Develop and maintain ELT/ETL workflows that ingest data from databases, APIs, event streams, and files.
• Model data using dimensional and lakehouse approaches to support analytics and ML use cases.
• Tune performance and manage cost across Databricks clusters and Snowflake warehouses.
• Implement data quality checks, monitoring, alerting, and lineage to ensure reliability and trust in data.
• Apply data governance and security best practices, including access control, masking, and compliance requirements.
• Build CI/CD and infrastructure-as-code practices for data pipelines.
• Collaborate with stakeholders to understand requirements and translate them into technical solutions.
• Document pipelines and data models, and mentor junior engineers through code reviews and knowledge sharing.

Conocimientos

Advanced SQL

Python

Data Modeling

ETL/ELT design

Herramientas

Databricks

Snowflake

Airflow

Azure Data Factory

Git

CI/CD

Descripción del empleo

About the Role

Our clientis looking for a Data Engineer with hands‑on experience in Databricks and Snowflake to design, build, and maintain modern data pipelines and platforms.

You will work closely with analysts, data scientists, and product teams to turn raw data into trusted, high-quality datasets that drive analytics, reporting, and machine learning.

Key Responsibilities

• Design, build, and optimize scalable batch and streaming data pipelines using Databricks (Spark, Delta Lake) and Snowflake.


• Develop and maintain ELT/ETL workflows that ingest data from databases, APIs, event streams, and files.


• Model data using dimensional and lakehouse approaches (e.g., medallion architecture) to support analytics and ML use cases.


• Tune performance and manage cost across Databricks clusters and Snowflake warehouses.


• Implement data quality checks, monitoring, alerting, and lineage to ensure reliability and trust in data.


• Apply data governance and security best practices, including access control, masking, and compliance requirements.


• Build CI/CD and infrastructure-as-code practices for data pipelines.


• Collaborate with stakeholders to understand requirements and translate them into technical solutions.


• Document pipelines and data models, and mentor junior engineers through code reviews and knowledge sharing.

Required Qualifications

• 3 to 7 years of professional experience in data engineering.


• Strong hands‑on experience with Databricks (Spark, PySpark/Spark SQL, Delta Lake, Workflows/Jobs).


• Strong hands‑on experience with Snowflake (warehousing, SQL, Snowpipe, Streams & Tasks, performance tuning).


• Advanced SQL skills and proficiency in Python (Scala is a plus).


• Solid understanding of data warehousing, data modeling, and ETL/ELT design patterns.


• Experience with at least one major cloud platform (AWS, Azure, or GCP) and its storage and compute services.


• Experience with orchestration tools such as Airflow, Databricks Workflows, or Azure Data Factory.


• Familiarity with version control (Git) and CI/CD practices.


• Strong problem‑solving, communication, and collaboration skills.