Data Lead Data Engineering
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Summary
Lead data engineering role in Singapore (7-12 years' experience) driving diagnostics, optimisation and stabilisation of enterprise ETL and batch platforms. Core stack: Azure Data Factory, Databricks (PySpark/Spark SQL), SSIS, SQL Server, plus Control-M/Autosys orchestration and Azure Monitor monitoring.
Experience: 7-12 Years
Location: Singapore
Fulltime Permanent Role
We are looking for a
Data Lead - Data Engineering
to drive technical diagnostics, optimisation, and stabilisation 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
Data Lead - Data Engineering
to drive technical diagnostics, optimisation, and stabilisation 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