The role will be responsible for setting up the data warehouses necessary to handle large volumes of data, create meaningful analyses, and deliver recommendations to leadership. Core Responsibilities Create and maintain optimal data pipeline architecture ETL/ ELT into structured data Assemble large, complex data sets that meet business requirements and create and maintain multi-dimensional modelling like Star Schema and Snowflake Schema, normalisation, de-normalization, joining of datasets. Expert level experience in creating a 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, optimising data delivery and re-designing infrastructure for greater scalability. 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. Setup Lambda, code pipeline (CI/CD), Glue, S3, Redshift needs to be maintained for uninterrupted automation. Build analytics tools that utilize the data pipeline to provide actionable insight into customer acquisition, operational efficiency, and other key business performance metrics. 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. Implement best practices for code reviews, branching strategies, and continuous integration. Create data tools for analytics and data scientist team members that assist them in building and optimising our product into an innovative industry leader Ensure data privacy and compliance with relevant regulations (e.g., GDPR) when handling customer data. Maintain data quality and consistency within the application, addressing data-related issues as they arise. Requirements Required 4-8 years of relevant experience Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases and Cloud Data warehouse like AWS Redshift Experience in creating scalable, efficient schema designs to support diverse business needs. Experience with database normalization, schema evolution, and maintaining data integrity Proactively share best practices, contributing to team knowledge and improving schema design transitions. Develop data models, create dimensions and facts, and establish views and procedures to enable automation programmability. Collaborate effectively with cross-functional teams to gather requirements, incorporate feedback, and align analytical work with business objectives Prior Data Modelling, OLAP cube modelling Data compression into PARQUET to improve processing and finetuning SQL programming skills. Experience building and optimizing “big data” data pipelines, architectures and data sets. Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement. Experience with manipulating, processing and extracting value from large disconnected unrelated datasets Strong analytic skills related to working with structured and unstructured datasets. Working knowledge of message queuing, stream processing, and highly scalable “big data” stores. Experience supporting and working with cross-functional teams and Global IT. Familiarity of working in an agile based working models. Preferred Qualifications/Expertise Experience with relational SQL and NoSQL databases, especially AWS Redshift. Experience with AWS cloud services Preferable: S3, EC2, Lambda, Glue, EMR, Code pipeline highly preferred. Experience with similar services on another platform would also be considered. Education: Bachelor’s or master’s degree on Technology and Computer Science background
