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Open nowPosted 21 days ago

Senior Data Engineer (Banking, 1-year renewable contract)

MyCareersFuture94,028 open roles

Pay
SGD 7,500 – SGD 10,500 a month
Where
Singapore
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Your applicationOpen nowSenior Data Engineer (Banking, 1-year renewable contract)MyCareersFuture · Singapore

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The clock on this job

Early applications get read.

7.7% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
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  2. 3.3%3 days
  3. 7.7%7 days
  4. 14.0%14 days
  5. 33.7%30 days
This job: posted 21 days ago

The posting

Dear Applicant,

If you or someone you know is interested, please send the CV directly to [email protected] (most preferred, as I may overlook some CVs due to the high volume).

Please note that visa sponsorship is not available at this time.

Key Responsibilities

  • Implement and operationalize enterprise-scale Lakehouse platforms, data products, and data marketplace capabilities.
  • Design, develop, test, and maintain scalable batch, streaming, CDC, and API-based data ingestion pipelines.
  • Develop multimodal data ingestion pipelines, including:

o Content extraction from various file formats.

o Regex-based extraction of specific fields.

o Content extraction from embedded images.

o Frame extraction from video files.

o Transcript extraction from audio files.

  • Build and maintain foundation and business data products with defined data contracts, SLAs, data quality controls, and governance standards.
  • Implement and work with modern open table formats, including Iceberg, Hudi, and Delta Lake.
  • Develop and support data pipelines for RAG, vector search, Generative AI (GenAI), and agentic AI use cases.
  • Perform performance tuning, optimization, production support, troubleshooting, and root cause analysis across data platforms and pipelines.
  • Implement data ingestion, transformation, reconciliation, and data quality frameworks.
  • Develop data architectures supporting NLP and AI-driven analytics, including the ingestion, curation, governance, and management of structured and unstructured data.
  • Support ML platforms and workflows, including model development, deployment, and operationalization.
  • Develop internal engineering tools and full-stack applications using Python, shell scripting, and modern web frameworks.
  • Expose and integrate data through APIs, event streams, dashboards, and BI platforms.
  • Create and maintain technical documentation, deployment guides, and operational runbooks.
  • Ensure compliance with engineering standards, DevSecOps controls, CI/CD practices, security requirements, and software delivery standards.
  • Collaborate effectively with distributed engineering, data, technology, and business teams across multiple projects.
  • Drive continuous improvement in data platform reliability, scalability, automation, and operational excellence.

Key Requirements

  • Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related discipline.
  • 8–12 years of experience in Data Engineering, Big Data, Data Lake, Lakehouse, or large-scale data platform implementations.
  • Strong hands-on experience with enterprise data platforms such as Databricks, Snowflake, Cloudera, Azure, AWS, GCP, Huawei, or Alibaba Cloud.
  • Hands-on experience designing and developing data products and data marketplace capabilities.
  • Strong expertise in Spark, PySpark, SQL, Python, and/or Scala, with strong overall programming skills.
  • Proven experience building scalable data ingestion, transformation, streaming, CDC, reconciliation, and data quality pipelines.
  • Hands-on experience with Iceberg, Hudi, Delta Lake, and object storage platforms.
  • Experience with modern data technologies such as Kafka, Flink, Spark Streaming, Airflow, Trino, Dremio, Hive, and/or Impala.
  • Strong hands-on experience with Kubernetes, OpenShift, Docker, Terraform, Jenkins, Git, CI/CD, MLflow, and observability tools.
  • Experience designing data architectures and pipelines supporting NLP, AI-driven analytics, RAG, vector search, GenAI, and agentic AI use cases.
  • Experience handling and governing unstructured and multimodal data, including documents, images, audio, and video.
  • Experience with ML platforms and frameworks such as CML, Spark MLlib, scikit-learn, and XGBoost, including model deployment.
  • Strong knowledge of data modelling, metadata management, data lineage, data governance, and data quality.
  • Experience exposing data through APIs, event streams, dashboards, and BI platforms.
  • Experience with full-stack/internal engineering tool development using Python, shell scripting, Flask, React, or similar technologies is advantageous.
  • Knowledge or experience with Teradata, Netezza, Greenplum, or MPP migration programmes is advantageous.
  • Strong engineering, automation, troubleshooting, and performance optimization mindset.
  • Strong communication and stakeholder management skills, with the ability to work effectively across distributed teams and multiple projects.
  • Experience working in Agile delivery environments and enterprise-scale technology platforms.
  • Strong commitment to quality, operational excellence, automation, and continuous improvement.
  • Relevant certifications such as Databricks Certified Data Engineer, Azure Data Engineer Associate, AWS Data Analytics Specialty, Google Professional Data Engineer, SnowPro, or DAMA CDMP would be an advantage.
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