Data Engineer

Overview

Tyde Digital, a trusted business optimization consultancy, is looking for a highly analytical and detail-oriented Data Engineer to join our rapidly growing team. At Tyde, we collaborate with businesses across diverse industries and regions, helping them enhance four key areas of their operations :

people, processes, technology, and data . Our goal is to empower organizations with data driven decision-making, enabling them to achieve their strategic objectives. Responsibilities

Data Pipeline Development :

Design and implement scalable and efficient architecture for data pipelines, ensuring seamless data flow across multiple endpoints. Technical Customizations :

Deploy technical customizations to pre-packaged software solutions. Micro-service and API Development :

Create and deploy micro-services and APIs to facilitate data transfer and integration between various systems. Data Transformation :

Perform data transformation activities, including data cleansing, data mining, and data processing, utilizing a variety of technologies and tools. Continuous Improvement :

Collaborate with cross-functional teams to drive changes and enhancements based on data-driven findings. Data Mapping :

Identify and present key performance indicators (KPIs) based on existing data points and datasets. Solution Development :

Design and implement end-to-end data-driven solutions tailored to business requirements, integrating tools, platforms, and custom logic to address specific operational challenges. Involvement In

Collecting Requirements from Clients :

Identify and present key gaps and opportunities for business optimization from the client. Creating Data-Driven Insights :

Provide valuable insights derived from data analysis and contribute to strategic decision-making processes. Reporting and Visualization :

Develop reporting tools using a range of Business Intelligence (BI) tools such as Power BI, Tableau, QlikView, and others. KPI Identification :

Identify and present key performance indicators (KPIs) based on existing data points and datasets.

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