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Open nowPosted 21 days ago

Data Lead

MyCareersFuture94,028 open roles

Pay
SGD 10,500 – SGD 11,000 a Monthly
Where
Central, Singapore
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Your applicationOpen nowData LeadMyCareersFuture · Central, Singapore

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on MyCareersFuture's own form.

The reply lands in your private mailbox

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The clock on this job

Early applications get read.

7.8% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 1.6%1 day
  2. 3.4%3 days
  3. 7.8%7 days
  4. 14.3%14 days
  5. 33.7%30 days
This job: posted 21 days ago

The posting

We are looking for a Data Lead – Data Engineering to drive technical diagnostics, optimization, and stabilization of enterprise data platforms supporting large-scale ETL and batch processing systems.

Key Responsibilities

• Strong expertise in: Azure Data Factory (ADF),Databricks (PySpark / Spark SQL preferred),SSIS and SQL Server

• Hands-on experience with: Batch orchestration tools (Control-M / Autosys),ETL/ELT pipeline design and optimization

• Strong experience in: SQL development and performance tuning, Troubleshooting and debugging complex data pipelines

• Familiarity with: Monitoring and alerting tools (Azure Monitor, Log Analytics)

• Exposure to large-scale data processing and batch system

• Review and optimize ADF pipelines, SSIS packages, and Databricks workflows for performance, scalability, and

• Analyze batch scheduling and orchestration frameworks (Control-M / Autosys), including dependencies, triggers, and SLA adherence

• Investigate and resolve pipeline failures, job delays, and runtime issues, ensuring faster recovery and minimal business

• Evaluate source-to-target integration flows, SQL logic, and transformation layers for efficiency and correctness

• Define and improve error handling, retry mechanisms, restart ability, and reprocessing strategies across ETL and batch

• Optimize runtime performance of data pipelines, including query tuning and batch execution improvements

• Establish and enhance monitoring and alerting frameworks using tools like Azure Monitor, Control-M dashboards, and custom logging solutions

• Identify automation opportunities to reduce manual intervention and improve operational efficiency

• Ensure data quality, consistency, and reliability across ETL processes and reporting outputs

• Support modernization initiatives, including migration from legacy ETL (SSIS) to cloud-based platforms like Databricks

• Collaborate with data engineers, architects, business, and support teams to drive improvements and ensure stable production systems

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    The review screen in CV Rocket: how each CV was read, round by round.

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3×

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