Job Description

Work in close partnership with the business leadership team to execute the analytics agenda Identify and incubate best-in-class external partners to drive delivery on strategic projects Develop custom models/algorithms to uncover signals/patterns and trends to drive long-term business performance Execute the business analytics program agenda using a methodical approach that conveys to stakeholders what business analytics will deliver What you will bring Using data analysis to make recommendations to senior leaders Technical experience in roles in best-in-class analytics practices Experience deploying new analytical approaches in a complex and highly matrixed organization Savvy in usage of the analytics techniques to create business impacts In This Role As a DaaS Data Engineer, you will have the opportunity to design and build scalable, secure, and cost-effective cloud-based data solutions. You will develop and maintain data pipelines to extract, transform, and load data into data warehouses or data lakes, ensuring data quality and validation processes to maintain data accuracy and integrity. You will ensure efficient data storage and retrieval for optimal performance, and collaborate closely with data teams, product owners, and other stakeholders to stay updated with the latest cloud technologies and best practices. Role & Responsibilities: Design and Build: Develop and implement scalable, secure, and cost-effective cloud-based data solutions. Manage Data Pipelines: Develop and maintain data pipelines to extract, transform, and load data into data warehouses or data lakes. Ensure Data Quality: Implement data quality and validation processes to ensure data accuracy and integrity. Optimize Data Storage: Ensure efficient data storage and retrieval for optimal performance. Collaborate and Innovate: Work closely with data teams, product owners, and stay updated with the latest cloud technologies and best practices to remain current in the field. Technical Requirements: Programming: Python, PySpark, Go/Java Database: SQL, PL/SQL ETL & Integration: DBT, Databricks + DLT, AecorSoft, Talend, Informatica/Pentaho/Ab-Initio, Fivetran. Data Warehousing: SCD, Schema Types, Data Mart. Visualization: Databricks Notebook, PowerBI, Tableau, Looker. GCP Cloud Services: Big Query, GCS, Cloud Function, PubSub, Dataflow, DataProc, Dataplex. AWS Cloud Services: S3, Redshift, Lambda, Glue, CloudWatch, EMR, SNS, Kinesis. Azure Cloud Services: Azure Datalake Gen2, Azure Databricks, Azure Synapse Analytics, Azure Data Factory, Azure Stream Analytics. Supporting Technologies: Graph Database/Neo4j, Erwin, Collibra, Ataccama DQ, Kafka, Airflow. Experience with RGM.ai product would have an added advantage. Soft Skills: Problem-Solving: The ability to identify and solve complex data-related challenges. Communication: Effective communication skills to collaborate with Product Owners, analysts, and stakeholders. Analytical Thinking: The capacity to analyse data and draw meaningful insights. Attention to Detail: Meticulousness in data preparation and pipeline development. Adaptability: The ability to stay updated with emerging technologies and trends in the data engineering field.