Drive end-to-end execution of data analytics initiatives efficiently with completed and qualified data, and within the project timeline.
Build ETL data pipeline for data warehouse transformation.
Collaborate with business units, IT teams and internal team members effectively to ensure data are captured, understood, and translated for data analytics.
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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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Architecture & Pipeline: Design and govern data model (Medallion architecture) and oversee the implementation (e.g. data lakes, build automated batch / real-time streaming pipelines, data source integrations)
Collaborations: Partnering with Analysts
Governance: Establish technical best practices and enforcing data quality
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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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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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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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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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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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Design — Design and maintain scalable ELT pipelines ingesting 23M+ loyalty events monthly across AWS (Athena, S3, Glue, Lambda) and GCP (BigQuery, Pub/Sub).
Lead orchestration — Build and operate production pipelines in Dagster, including scheduling, dependency management, retries, and observability. Migrate any legacy Airflow workflows.
Drive platform reliability — Own pipeline monitoring, incident response, data quality enforcement, and SLA management across the ingestion layer.
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