Job Responsibilities:

- Create and maintain optimal data pipeline architecture for ETL/ELT into structured data.

- Assemble large, complex data sets that meet business requirements and create multi-dimensional modelling like Star Schema and Snowflake Schema.

- Expert level experience in creating scalable data warehouse including Fact tables, Dimensional tables and ingest datasets into cloud-based tools.

- Identify, design, and implement internal process improvements including automating manual processes and optimising data delivery.

- Collaborate with stakeholders to ensure seamless integration of data with internal data marts, enhancing advanced reporting.

- Setup and maintain data ingestion, streaming, scheduling, and job monitoring automation using AWS services including Lambda, Code Pipeline, Glue, S3, and Redshift.

- Build analytics tools that utilize the data pipeline to provide actionable insight into customer acquisition and operational efficiency.

- Work with stakeholders to assist with data-related technical issues and support their data infrastructure needs.

- Utilize GitHub for version control, code collaboration, and repository management.

- Create data tools for analytics and data scientist team members.

- Ensure data privacy and compliance with relevant regulations when handling customer data.

- Maintain data quality and consistency within the application.

Requirements:

- Must have at least 4+ years of hands-on data engineering experience with the recent 2+ years in AWS cloud data warehouses and AWS cloud services.

- Must have advanced SQL knowledge and hands-on experience with relational databases and query authoring, plus a cloud data warehouse like AWS Redshift.

- Must have expert-level experience creating scalable data warehouses - Fact tables, Dimensional tables, and ingesting datasets into cloud-based tools.

- Must have strong multi-dimensional data modelling experience - Star Schema, Snowflake Schema, normalisation/de-normalisation, joins, OLAP cube modelling, and schema evolution while maintaining data integrity.

- Must have hands-on experience creating and maintaining optimal ETL/ELT data pipeline architecture into structured data.

- Must have experience setting up and maintaining data ingestion, streaming, scheduling, and job-monitoring automation using AWS services - Lambda, Glue, S3, Redshift, and Code Pipeline (CI/CD).

- Must have experience building and optimizing big-data pipelines, architectures, and datasets, including data compression into PARQUET and SQL performance tuning.

- Must have experience with GitHub for version control, code collaboration, code reviews, branching strategies, and continuous integration.

- Must have strong analytical skills across structured and unstructured datasets, with experience performing root-cause analysis to answer business questions and identify improvements.

- Must have experience collaborating with cross-functional teams and Global IT to gather requirements and align work with business objectives, and ensuring data privacy/compliance (e.g. GDPR).

- Working knowledge of message queuing, stream processing, and highly scalable big-data stores; familiarity with Agile working models.

- Current or recent role must clearly demonstrate hands-on work with AWS data platforms and ETL pipeline implementation - resume must describe specific projects, tools used, and candidate's direct scope.

- Bachelor's or master's degree on Technology and Computer Science background.

- Must be an immediate joiner or currently serving notice period, able to start within the next week.