{"id":574,"date":"2026-08-08T11:39:33","date_gmt":"2026-08-08T11:39:33","guid":{"rendered":"https:\/\/cybotrix.com\/careers\/?p=574"},"modified":"2026-08-08T11:39:33","modified_gmt":"2026-08-08T11:39:33","slug":"data-engineer-jobs-in-singapore","status":"publish","type":"post","link":"https:\/\/cybotrix.com\/careers\/data-engineer-jobs-in-singapore","title":{"rendered":"Data Engineer Jobs in Singapore | Cloud & Big Data Careers"},"content":{"rendered":"\n
Job Overview<\/h2>\n\n\n\n
Explore Data Engineer Jobs in Singapore<\/strong> 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<\/strong>.<\/p>\n\n\n\n
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<\/strong>.<\/p>\n\n\n\n\n\n\n
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<\/strong>.<\/p>\n\n\n\n
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.<\/p>\n\n\n\n
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Job Title<\/h3>\n\n\n\n
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Position:<\/strong> Data Engineer \/ Cloud Data Engineer \/ Senior Data Engineer Location:<\/strong> Singapore Employment Type:<\/strong> Full-Time \/ Permanent \/ Contract Opportunities Work Model:<\/strong> On-site \/ Hybrid \/ Remote, depending on the opportunity Department:<\/strong> Data Engineering \/ Cloud Engineering \/ Data & Analytics Career Level:<\/strong> Entry Level, Associate, Mid-Level, Senior & Lead Primary Domain:<\/strong> Data Engineering, Big Data, Cloud & Data Platforms<\/p>\n<\/blockquote>\n\n\n\n\n\n\n\n\n\n\n
Salary Range<\/h3>\n\n\n\n
Indicative salary ranges vary according to experience, technical specialization, employer, industry, project complexity, and total compensation structure.<\/p>\n\n\n\n
Career Level<\/th>
Indicative Monthly Base Salary (SGD)<\/th>
Indicative Annual Base Salary (SGD)<\/th><\/tr><\/thead>
Note:<\/strong> 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<\/strong>.<\/p>\n\n\n\n\n\n\n\n\n\n\n
Roles & Responsibilities<\/h3>\n\n\n\n
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Design, develop, test, deploy, and maintain scalable data pipelines and data processing platforms<\/strong>.<\/li>\n\n\n\n
Build reliable ETL and ELT workflows to ingest, transform, validate, and deliver structured and unstructured data.<\/li>\n\n\n\n
Develop data engineering solutions using Python, SQL, PySpark, Apache Spark, and distributed processing frameworks<\/strong>.<\/li>\n\n\n\n
Design and maintain cloud-based data platforms using AWS, Microsoft Azure, or Google Cloud Platform<\/strong>.<\/li>\n\n\n\n
Build modern data lakes, data warehouses, and lakehouse architectures<\/strong> for enterprise analytics.<\/li>\n\n\n\n
Develop and optimize solutions using Databricks, Snowflake, Amazon Redshift, Google BigQuery, or Azure Synapse Analytics<\/strong>.<\/li>\n\n\n\n
Build batch and real-time data processing pipelines for high-volume datasets.<\/li>\n\n\n\n
Implement event-driven and streaming data solutions using Apache Kafka, AWS Kinesis, or comparable technologies<\/strong>.<\/li>\n\n\n\n
Develop workflow orchestration pipelines using Apache Airflow, Azure Data Factory, AWS Glue, or similar tools<\/strong>.<\/li>\n\n\n\n
Create reusable transformation workflows using dbt<\/strong> and modern analytics engineering practices.<\/li>\n\n\n\n
Integrate data from relational databases, NoSQL systems, APIs, SaaS applications, files, streaming platforms, and third-party sources.<\/li>\n\n\n\n
Design scalable data models to support analytics, reporting, BI, Machine Learning, and AI applications.<\/li>\n\n\n\n
Implement data quality validation, reconciliation, monitoring, lineage, metadata management, and governance controls.<\/li>\n\n\n\n
Optimize SQL queries, Spark jobs, data partitioning, storage formats, and compute resources for performance and cost.<\/li>\n\n\n\n
Implement secure access to enterprise datasets through encryption, IAM, RBAC, masking, and appropriate security controls.<\/li>\n\n\n\n
Build APIs or services where required to expose curated datasets to downstream applications.<\/li>\n\n\n\n
Support datasets and feature pipelines required by Data Scientists, AI Engineers, and Machine Learning Engineers<\/strong>.<\/li>\n\n\n\n
Implement CI\/CD and DataOps practices for reliable data platform releases.<\/li>\n\n\n\n
Monitor pipeline failures, latency, data freshness, infrastructure utilization, and production reliability.<\/li>\n\n\n\n
Troubleshoot data discrepancies, failed jobs, performance issues, and integration problems.<\/li>\n\n\n\n
Collaborate with Data Scientists, Data Analysts, BI Developers, Software Engineers, Cloud Engineers, DevOps teams, and business stakeholders.<\/li>\n\n\n\n
Maintain documentation for data models, pipelines, architecture, data lineage, and operational processes.<\/li>\n\n\n\n
Senior engineers may lead cloud data architecture, platform modernization, migration, governance, and technical mentoring initiatives.<\/li>\n<\/ul>\n\n\n\n\n\n\n\n\n\n\n
Technical Skill Set<\/h3>\n\n\n\n
Technical Area<\/th>
Skills \/ Technologies<\/th><\/tr><\/thead>
Programming<\/strong><\/td>
Python, SQL, Scala, Java<\/td><\/tr>
Big Data<\/strong><\/td>
Apache Spark, PySpark, Hadoop<\/td><\/tr>
Data Platforms<\/strong><\/td>
Databricks, Snowflake<\/td><\/tr>
ETL \/ ELT<\/strong><\/td>
ETL, ELT, Data Integration, Data Transformation<\/td><\/tr>
Orchestration<\/strong><\/td>
Apache Airflow, Azure Data Factory, AWS Glue<\/td><\/tr>
Data Lake, Data Warehouse, Data Lakehouse<\/td><\/tr>
Databases<\/strong><\/td>
PostgreSQL, MySQL, SQL Server, Oracle<\/td><\/tr>
NoSQL<\/strong><\/td>
MongoDB, DynamoDB, Cassandra<\/td><\/tr>
Data Modeling<\/strong><\/td>
Dimensional Modeling, Star Schema, Snowflake Schema<\/td><\/tr>
Storage Formats<\/strong><\/td>
Parquet, Avro, ORC, Delta Lake<\/td><\/tr>
Containers<\/strong><\/td>
Docker, Kubernetes<\/td><\/tr>
DevOps \/ DataOps<\/strong><\/td>
Git, Jenkins, GitHub Actions, CI\/CD<\/td><\/tr>
Infrastructure as Code<\/strong><\/td>
Terraform<\/td><\/tr>
Data Governance<\/strong><\/td>
Data Quality, Data Lineage, Metadata Management, Access Control<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n\n\n\n\n
Required Qualifications<\/h3>\n\n\n\n
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Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Technology, Software Engineering, Data Science<\/strong>, or a related technical discipline, or equivalent practical experience.<\/li>\n\n\n\n
Strong programming skills in Python<\/strong> and advanced working knowledge of SQL.<\/li>\n\n\n\n
Understanding of relational databases, data modeling, database design, and query optimization.<\/li>\n\n\n\n
Experience designing or developing ETL\/ELT pipelines<\/strong>.<\/li>\n\n\n\n
Knowledge of data warehousing, data lakes, and modern lakehouse architecture.<\/li>\n\n\n\n
Experience with Apache Spark, PySpark, or another distributed data processing framework.<\/li>\n\n\n\n
Familiarity with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform<\/strong>.<\/li>\n\n\n\n
Understanding of batch processing and real-time\/streaming data architectures.<\/li>\n\n\n\n
Experience integrating data from databases, APIs, applications, and file-based sources.<\/li>\n\n\n\n
Knowledge of Git and modern software development workflows.<\/li>\n\n\n\n
Understanding of data quality, validation, lineage, governance, and security concepts.<\/li>\n\n\n\n
Ability to troubleshoot complex data pipeline and production processing issues.<\/li>\n\n\n\n
Understanding of scalable and fault-tolerant data architecture.<\/li>\n\n\n\n
Ability to create maintainable, reusable, and well-documented data engineering solutions.<\/li>\n<\/ul>\n\n\n\n\n\n\n\n\n\n\n
Work Experience<\/h2>\n\n\n\n
Entry-Level \/ Junior Data Engineer<\/h3>\n\n\n\n
Candidates should demonstrate strong fundamentals in Python, SQL, databases, data structures, ETL, and cloud computing<\/strong> through internships, academic projects, certifications, personal projects, or relevant technical experience.<\/p>\n\n\n\n
Exposure to Spark, Databricks, Snowflake, Airflow, AWS, Azure, or GCP is advantageous.<\/p>\n\n\n\n
Mid-Level Data Engineer<\/h3>\n\n\n\n
Candidates should have practical experience building and maintaining production data pipelines, data warehouses, or cloud data platforms.<\/p>\n\n\n\n
Expected competencies may include Python, advanced SQL, ETL\/ELT, Apache Spark, PySpark, Airflow, cloud services, data modeling, and pipeline optimization<\/strong>.<\/p>\n\n\n\n
Senior Data Engineer<\/h3>\n\n\n\n
Senior professionals should demonstrate experience designing scalable enterprise data platforms and solving complex data architecture challenges.<\/p>\n\n\n\n
Strong expertise in technologies such as Databricks, Snowflake, Apache Spark, Kafka, AWS, Azure, GCP, Data Lakehouse, DataOps, and distributed data processing<\/strong> is highly valuable.<\/p>\n\n\n\n
Senior engineers may also mentor junior team members and define data engineering standards, architecture patterns, and technical best practices.<\/p>\n\n\n\n
Lead \/ Cloud Data Engineer<\/h3>\n\n\n\n
Lead-level professionals may own enterprise data architecture, cloud migration, data platform modernization, governance frameworks, performance optimization, and technical roadmaps<\/strong>.<\/p>\n\n\n\n
Experience designing highly available and cost-efficient platforms supporting analytics, AI, Machine Learning, and real-time business applications is advantageous.<\/p>\n\n\n\n
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Communication Skills<\/h3>\n\n\n\n
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Strong written and verbal communication skills for technical and business environments.<\/li>\n\n\n\n
Ability to explain complex data architectures, pipelines, and integration processes clearly.<\/li>\n\n\n\n
Ability to translate business data requirements into scalable engineering solutions.<\/li>\n\n\n\n
Strong collaboration skills across Data Science, Analytics, Software Engineering, Cloud, DevOps, and business teams.<\/li>\n\n\n\n
Ability to document data pipelines, schemas, architecture decisions, and operational procedures.<\/li>\n\n\n\n
Strong analytical and structured problem-solving ability.<\/li>\n\n\n\n
Ability to communicate data-quality issues and production incidents clearly.<\/li>\n\n\n\n
Comfortable participating in architecture reviews, code reviews, sprint planning, and technical discussions.<\/li>\n\n\n\n
Ability to manage multiple technical priorities while maintaining data accuracy and platform reliability.<\/li>\n\n\n\n
Strong ownership and accountability for production data pipelines.<\/li>\n\n\n\n
Ability to communicate technical risks, dependencies, and implementation trade-offs.<\/li>\n\n\n\n
Leadership, mentoring, and stakeholder-management skills are valuable for senior and lead roles.<\/li>\n<\/ul>\n\n\n\n\n\n\n\n
Similar Jobs in Singapore<\/h2>\n\n\n\n
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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\u2026<\/p>\n","protected":false},"author":1,"featured_media":440,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4,15,18],"tags":[79,80,81,103,133,196,197,198,204,206,208,226,252,295,373,443,457,459,512,515,532],"class_list":["post-574","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-data","category-cloud-engineer","category-data-engineer","tag-apache-airflow","tag-apache-kafka","tag-apache-spark","tag-aws","tag-big-data","tag-data-lakehouse","tag-data-modeling","tag-data-pipeline-development","tag-data-warehousing","tag-databricks","tag-dbt","tag-docker","tag-etl-elt","tag-google-cloud-platform-gcp","tag-microsoft-azure","tag-postgresql","tag-pyspark","tag-python","tag-singapore","tag-snowflake","tag-sql"],"yoast_head":"\n
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