Data Engineer Jobs in Singapore | Cloud & Big Data Careers

Job Overview

Explore Data Engineer Jobs in Singapore for professionals specializing in building scalable data pipelines, cloud data platforms, enterprise data warehouses, and real-time data processing systems. Opportunities span entry-level through senior and lead positions across Cloud Data Engineering, Big Data, Data Warehousing, ETL/ELT, Data Lakehouse, Streaming Analytics, and Data Platform Engineering.

Data Engineers are responsible for transforming raw information from multiple sources into reliable, secure, high-quality datasets that can support analytics, business intelligence, Artificial Intelligence, Machine Learning, and enterprise applications.

The role requires strong engineering knowledge across Python, SQL, Apache Spark, PySpark, Databricks, Snowflake, Apache Kafka, AWS, Microsoft Azure, Google Cloud Platform, Airflow, dbt, ETL/ELT, and data warehousing.

Experienced Data Engineers may take ownership of enterprise data architecture, cloud migration, lakehouse platforms, streaming systems, data governance, infrastructure optimization, and platforms supporting large-scale AI and Machine Learning workloads.

Job Title

Position: Data Engineer / Cloud Data Engineer / Senior Data Engineer
Location: Singapore
Employment Type: Full-Time / Permanent / Contract Opportunities
Work Model: On-site / Hybrid / Remote, depending on the opportunity
Department: Data Engineering / Cloud Engineering / Data & Analytics
Career Level: Entry Level, Associate, Mid-Level, Senior & Lead
Primary Domain: Data Engineering, Big Data, Cloud & Data Platforms

Salary Range

Indicative salary ranges vary according to experience, technical specialization, employer, industry, project complexity, and total compensation structure.

Note: Salary figures are indicative rather than guaranteed. Compensation can vary significantly for specialized expertise in Databricks, Snowflake, cloud architecture, Apache Spark, real-time streaming, DataOps, AI infrastructure, and enterprise data platforms.

Roles & Responsibilities

• Design, develop, test, deploy, and maintain scalable data pipelines and data processing platforms.
• Build reliable ETL and ELT workflows to ingest, transform, validate, and deliver structured and unstructured data.
• Develop data engineering solutions using Python, SQL, PySpark, Apache Spark, and distributed processing frameworks.
• Design and maintain cloud-based data platforms using AWS, Microsoft Azure, or Google Cloud Platform.
• Build modern data lakes, data warehouses, and lakehouse architectures for enterprise analytics.
• Develop and optimize solutions using Databricks, Snowflake, Amazon Redshift, Google BigQuery, or Azure Synapse Analytics.
• Build batch and real-time data processing pipelines for high-volume datasets.
• Implement event-driven and streaming data solutions using Apache Kafka, AWS Kinesis, or comparable technologies.
• Develop workflow orchestration pipelines using Apache Airflow, Azure Data Factory, AWS Glue, or similar tools.
• Create reusable transformation workflows using dbt and modern analytics engineering practices.
• Integrate data from relational databases, NoSQL systems, APIs, SaaS applications, files, streaming platforms, and third-party sources.
• Design scalable data models to support analytics, reporting, BI, Machine Learning, and AI applications.
• Implement data quality validation, reconciliation, monitoring, lineage, metadata management, and governance controls.
• Optimize SQL queries, Spark jobs, data partitioning, storage formats, and compute resources for performance and cost.
• Implement secure access to enterprise datasets through encryption, IAM, RBAC, masking, and appropriate security controls.
• Build APIs or services where required to expose curated datasets to downstream applications.
• Support datasets and feature pipelines required by Data Scientists, AI Engineers, and Machine Learning Engineers.
• Implement CI/CD and DataOps practices for reliable data platform releases.
• Monitor pipeline failures, latency, data freshness, infrastructure utilization, and production reliability.
• Troubleshoot data discrepancies, failed jobs, performance issues, and integration problems.
• Collaborate with Data Scientists, Data Analysts, BI Developers, Software Engineers, Cloud Engineers, DevOps teams, and business stakeholders.
• Maintain documentation for data models, pipelines, architecture, data lineage, and operational processes.
• Senior engineers may lead cloud data architecture, platform modernization, migration, governance, and technical mentoring initiatives.

Technical Skill Set

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Technology, Software Engineering, Data Science, or a related technical discipline, or equivalent practical experience.
• Strong programming skills in Python and advanced working knowledge of SQL.
• Understanding of relational databases, data modeling, database design, and query optimization.
• Experience designing or developing ETL/ELT pipelines.
• Knowledge of data warehousing, data lakes, and modern lakehouse architecture.
• Experience with Apache Spark, PySpark, or another distributed data processing framework.
• Familiarity with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform.
• Understanding of batch processing and real-time/streaming data architectures.
• Experience integrating data from databases, APIs, applications, and file-based sources.
• Knowledge of Git and modern software development workflows.
• Understanding of data quality, validation, lineage, governance, and security concepts.
• Ability to troubleshoot complex data pipeline and production processing issues.
• Understanding of scalable and fault-tolerant data architecture.
• Ability to create maintainable, reusable, and well-documented data engineering solutions.

Work Experience

Entry-Level / Junior Data Engineer

Candidates should demonstrate strong fundamentals in Python, SQL, databases, data structures, ETL, and cloud computing through internships, academic projects, certifications, personal projects, or relevant technical experience.

Exposure to Spark, Databricks, Snowflake, Airflow, AWS, Azure, or GCP is advantageous.

Mid-Level Data Engineer

Candidates should have practical experience building and maintaining production data pipelines, data warehouses, or cloud data platforms.

Expected competencies may include Python, advanced SQL, ETL/ELT, Apache Spark, PySpark, Airflow, cloud services, data modeling, and pipeline optimization.

Senior Data Engineer

Senior professionals should demonstrate experience designing scalable enterprise data platforms and solving complex data architecture challenges.

Strong expertise in technologies such as Databricks, Snowflake, Apache Spark, Kafka, AWS, Azure, GCP, Data Lakehouse, DataOps, and distributed data processing is highly valuable.

Senior engineers may also mentor junior team members and define data engineering standards, architecture patterns, and technical best practices.

Lead / Cloud Data Engineer

Lead-level professionals may own enterprise data architecture, cloud migration, data platform modernization, governance frameworks, performance optimization, and technical roadmaps.

Experience designing highly available and cost-efficient platforms supporting analytics, AI, Machine Learning, and real-time business applications is advantageous.

Communication Skills

• Strong written and verbal communication skills for technical and business environments.
• Ability to explain complex data architectures, pipelines, and integration processes clearly.
• Ability to translate business data requirements into scalable engineering solutions.
• Strong collaboration skills across Data Science, Analytics, Software Engineering, Cloud, DevOps, and business teams.
• Ability to document data pipelines, schemas, architecture decisions, and operational procedures.
• Strong analytical and structured problem-solving ability.
• Ability to communicate data-quality issues and production incidents clearly.
• Comfortable participating in architecture reviews, code reviews, sprint planning, and technical discussions.
• Ability to manage multiple technical priorities while maintaining data accuracy and platform reliability.
• Strong ownership and accountability for production data pipelines.
• Ability to communicate technical risks, dependencies, and implementation trade-offs.
• Leadership, mentoring, and stakeholder-management skills are valuable for senior and lead roles.

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