Responsibilities

Design, develop, test, and maintain optimal data pipeline and ETL architectures.
Map out data systems and define/design required integrations, ETL, BI, and AI systems/processes.
Prepare and optimise data for predictive and prescriptive modelling.
Collaborate with teams to integrate ERP data into the enterprise data lake, ensuring seamless flow and quality.
Enhance cloud data infrastructure on AWS or Azure for scalability and performance.
Utilise big data tools and frameworks to optimise data acquisition and preparation.
Build architectures to move data to/from data lakes and data warehouses for advanced analytics.
Develop and curate data models for analytics, dashboards, and reports.
Conduct code reviews, maintain production-level code, and implement testing approaches.
Monitor, troubleshoot, and resolve data ingestion workflows to maintain reliability and uptime.
Drive innovation and implement productive new approaches to data engineering tasks.

Requirements

Bachelor's degree in Computer Science, Mathematics, Engineering, or a related field.




5+ years of experience working with enterprise data platforms, including building and managing data lakes.
QX | Public: 3-5 years of experience designing and implementing data warehouse solutions.
Expertise in SQL, including developing stored procedures (SPs) and applying advanced data design concepts
Proficiency in Spark (Python/Scala) and Spark Streaming for real-time data pipelines.
Experience with AWS or Azure services (e. g., AWS Glue, Azure Data Factory, Redshift, Snowflake).
Familiarity with big data tools such as Apache Kafka, Apache Spark, or Flink.
Hands-on experience with orchestration tools (e. g., Apache Airflow, Prefect).
Knowledge of CI/CD processes, version control (e. g., Git, Jenkins), and deployment automation.
Strong problem-solving, communication, and collaboration skills.

Good-to-Have Skills

Experience in integrating ERP data into data lakes.
Experience with traditional ETL tools (e. g

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