About this role
<div> <div> </div> <div> </div> <div> </div> <div> <p><span>Role Mission:</span><span> </span></p> </div> <div> <p><span>To provide technical leadership within StarHub’s Digital Experience Platform (DXP) Data organization by designing, delivering, and operationalizing complex data pipelines, curated datasets, and reusable engineering patterns on the cloud-native data platform. This role drives technical excellence across data ingestion, transformation, modeling, DataOps, and production reliability to enable trusted, scalable, and self-service analytics across business domains.</span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span>Accountabilities</span><span>:</span></p> </div> <div> <ol style="list-style-type:decimal" start="1"> <li> <p><span>Own </span><span>technical delivery</span><span> of complex, high-impact data engineering initiatives across ingestion, </span><span>transformation</span><span>, modeling, and operational stabilization.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="2"> <li> <p><span>Serve as the </span><span>senior technical leader </span><span>within the Data Engineering function, setting implementation direction, reviewing design quality, and uplifting engineering standards across the team.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="3"> <li> <p><span>Drive </span><span>production</span><span> reliability, observability, and root-cause elimination for critical pipelines and datasets.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="4"> <li> <p><span>Develop reusable </span><span>engineering patterns</span><span>, frameworks, and automation to improve delivery speed, quality, and maintainability.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="5"> <li> <p><span>Partner</span><span> with Data Architecture, Platform Engineering, Data Quality Stewards, BI, and business stakeholders to translate requirements into trusted and scalable data products.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="6"> <li> <p><span>Coach and mentor</span><span> engineers through design reviews, code reviews, troubleshooting, and day-to-day technical guidance without direct people management responsibility.</span><span> </span></p> </li> </ol> </div> <div> <p><span> </span></p> </div> <div> <p><span>Responsibilities</span><span>: </span><span> </span></p> </div> </div> <div> <div> <ol style="list-style-type:decimal" start="1"> <li> <p><span>Technical Delivery & Solution Design</span><span>: Lead design and implementation of complex ingestion, transformation, and curated data model solutions across Datapipe, Snowflake, and AWS, ensuring scalable, reusable, and cost-efficient patterns.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="2"> <li> <p><span>Engineering Standards & Quality</span><span>: Establish and enforce practical engineering standards across SQL, Python, DAG design, CI/CD, testing, observability, RBAC-aware implementation, and cost-aware design.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="3"> <li> <p><span>Operational Excellence</span><span>: Own production stability for critical pipelines and datasets, including incident triage, recovery leadership, RCA, and preventative improvement actions.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="4"> <li> <p><span>Reusable Enablement:</span><span> Build reusable components, templates, runbooks, and agentic delivery patterns to reduce duplicated effort, improve maintainability, and raise engineering velocity.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="5"> <li> <p><span>Data Quality & Trusted Data</span><span>: Embed automated data quality controls into pipelines and curated layers, including validation, anomaly detection, reconciliation, and schema drift checks.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="6"> <li> <p><span>Collaboration & Enablement:</span><span> Work with architects, stewards, platform engineers, BI teams, and business stakeholders to shape requirements into implementable data contracts and trusted datasets for self-service analytics.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="7"> <li> <p><span>Technical Leadership by Influence:</span><span> Act as the senior technical escalation point for difficult engineering and production issues, while coaching Senior Data Engineers and Data Engineers through design and implementation guidance.</span><span> </span></p> </li> </ol> </div> <div> <p><span> </span></p> </div> <div> <p><span>Team Scope/ Stakeholders:</span><span> </span></p> </div> <div> <ol style="list-style-type:decimal" start="1"> <li> <p><span>Scope</span><span>: Complex pipelines, curated datasets, reusable engineering patterns, and production reliability across the DXP Data Platform (C360, Datapipe ingestion solution based on Apache Airbyte & Airflow, Snowflake, SageMaker, Cloud native skills).</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="2"> <li> <p><span>Decision Rights</span><span>: Technical design decisions within assigned initiatives, implementation patterns, code quality expectations, incident recovery actions, and recommendations on engineering prioritization and standards uplift.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="3"> <li> <p><span>Stakeholders</span><span>: Data Engineering, Platform Engineering, Architecture & Governance, BI, Data Science, Data Quality Stewards, Business Data Owners, Infrastructure, Cybersecurity/ISO, and Application domain teams.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="4"> <li> <p><span>Resources</span><span>: Individual contributor role operating as the senior-most hands-on engineer within the Data Engineering team, with responsibility to guide and uplift engineers across Singapore, Malaysia and India through technical leadership.</span><span> </span></p> </li> </ol> </div> <div> <p><span> </span></p> </div> <div> <p><span>Minimum Profile/ Track Record:</span><span> </span></p> </div> <div> <ol style="list-style-type:decimal" start="1"> <li> <p><span>7–10+</span><span> years of experience in cloud-native data engineering, with strong hands-on architecture, delivery, and production support experience on AWS & Snowflake.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="2"> <li> <p><span>Strong track record </span><span>delivering complex data engineering initiatives</span><span> independently, with the ability to operate across both build and run responsibilities.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="3"> <li> <p><span>Experience </span><span>partnering</span><span> with BI and business teams to design modelled datasets and enable self-service analytics.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="4"> <li> <p><span>Demonstrated </span><span>technical leadership</span><span> through design reviews, code reviews, mentoring, and troubleshooting guidance without formal team management responsibility.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="5"> <li> <p><span>Deep </span><span>hands-on technical expertise</span><span>, including:</span><span> </span></p> </li> </ol> </div> </div> <div> <div> <ol style="list-style-type:lower-alpha" start="1"> <li> <p><span>Snowflake</span><span>: schema design, Streams/Tasks, Stored Procedures, UDFs, RBAC-aware development, performance tuning, cost monitoring, Cortex AI, and Streamlit.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:lower-alpha" start="2"> <li> <p><span>Airflow or similar data orchestration tools</span><span>: DAG design, orchestration, scheduling, dependency management, retry patterns, and observability.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:lower-alpha" start="3"> <li> <p><span>Python and SQL</span><span>: pipeline scripting, transformation logic, data validation, and operational tooling.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:lower-alpha" start="4"> <li> <p><span>ELT/ETL frameworks</span><span>: Airbyte, Fivetran, and custom connector understanding or development.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:lower-alpha" start="5"> <li> <p><span>AWS services</span><span>: S3 (data lake structures and archival), Lambda, KMS, Transfer Family, CloudWatch, and SageMaker.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="6"> <li> <p><span>Demonstrated success delivering </span><span>medallion architecture (Bronze/Silver/Gold)</span><span> and enabling </span><span>self-service</span><span> data use cases.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="7"> <li> <p><span>Experience implementing automated </span><span>data quality</span><span> controls, remediation workflows, and data lineage-aware engineering practices across enterprise datasets.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="8"> <li> <p><span>Familiarity with </span><span>machine learning or AI integration</span><span> using platforms like AWS SageMaker.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="9"> <li> <p><span>Proven ability to troubleshoot </span><span>complex data issues</span><span>, lead root-cause analysis, and improve production stability through mechanisms rather than repeated manual intervention.</span><span> </span></p> </li> </ol> </div> <div> <ol style="list-style-type:decimal" start="10"> <li> <p><span>Track record of raising team </span><span>engineering quality</span><span> through reusable patterns, operational discipline, and technical coaching.</span><span> </span></p> </li> </ol> </div> <div> <p><span> </span></p> </div> </div>