Assist the Team Lead (Analytics) to ensure Analytics and Digital Transformation solutions are delivered in a timely and reliable manner, leveraging modern cloud-based SaaS data platforms such as Snowflake and enterprise-grade data integration tools.
Execute tasks to analyse business requirements, conduct research, and implement enterprise data warehouse solutions using Snowflake as the core SaaS data platform.
Design, test, and develop scalable data warehouse data structures and ELT/ETL pipelines to ingest data from various source systems into Snowflake and downstream semantic / analytics models.
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Design, develop, and maintain scalable ETL/ELT pipelines to extract, transform, and load data from databases, APIs, cloud platforms, and third-party systems.
Design, develop, integrate, test, and maintain RESTful APIs to enable secure and reliable data exchange between enterprise applications and external services.
Develop and optimise data integration processes to ensure accurate, consistent, and timely data availability.
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Design, develop, and maintain scalable ETL/ELT pipelines to extract, transform, and load data from databases, APIs, cloud platforms, and third-party systems.
Design, develop, integrate, test, and maintain RESTful APIs to enable secure and reliable data exchange between enterprise applications and external services.
Develop and optimise data integration processes to ensure accurate, consistent, and timely data availability.
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Upstream Source Systems: You will partner with core retail systems (POS, ERP, CRM, Supply Chain) to capture raw data cleanly and design robust integration pathways and make sure they are documented for the future
Common Data Engineering Team (Under your remit): This squad focuses on integrating raw data from diverse source systems, structuring it into our centralized, retail-specific common data model, and enforcing strict quality and compliance rules set by our Data Governance team.
Functional Data Engineering Team (Under your remit): This squad pulls data from the common data lake and refines, aggregates, and massages it to be explicitly fit-for-purpose for specialized business use cases.
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:Interpret and analyze complex data-related problems
.Conceive, prioritize, and plan data projects aligned with organizational goals
.Develop and implement databases, data collection systems, data analytics solutions, and other strategies to optimize statistical efficiency and quality
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Root Cause Analysis: Solves and works through complex problems and projects via in-depth evaluation of business processes, system processes, and industry standards; performs root cause analyses.
Data Exploration: Utilizes ad-hoc techniques to perform on-the-fly analysis of data.
Strategic Evaluation: Provides evaluative judgment based on the analysis of factual information in complicated and unique situations.
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Review video recordings and multimodal data collected by the data collection team to ensure they meet defined quality and capture standards (e.g. correct camera angles, SKU labels visibility, motion completeness, dual-camera coverage).
Verify collected data aligns with project specifications, including SKU selection criteria, shelf height parameters, environmental coverage, and annotation readiness.
Identify, flag, and reject non-conforming or incomplete recordings, providing clear feedback and rejection reasons for re-collection.
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We're building the future of enterprise automation at Able Perfect. If you're passionate about AI, data science, and solving real-world business problems, this is your launch pad.
As a Data Science & Automation Engineering Intern, you'll work alongside our core team to design, build, and deploy intelligent systems that automate critical business workflows, optimize departmental processes, and implement next-generation data platforms.
High performers in this program will be offered full-time conversion.
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