Junior Data Engineer

ECS is seeking a Jr. Data Engineer to work in our Remote office.

We are seeking a versatile and highly skilled Jr. Data Engineer to join our dynamic team. This role is ideal for an individual with experience in Python development, Robotic Process Automation (RPA), data analysis, data engineering support through building and managing data pipelines, business intelligence (BI) using PowerBI or Tableau, and PowerApps Development. The successful candidate will play a key role in developing solutions, automating data processes, conditioning datasets, supporting data engineering efforts, and providing actionable insights to drive data-driven decision-making within the organization.

Key Responsibilities:

• Data Development: Utilize Python to build scalable data pipelines, automate data workflows, and develop data processing scripts for transforming and conditioning datasets.
• RPA Development: Design and implement RPA solutions to automate repetitive and time-consuming tasks, improving operational efficiency.
• Business Intelligence (PowerBi): Design and develop interactive dashboards and reports using PowerBI to provide stakeholders with real and near-real time data insights and visualizations. Implement BI solutions that empower business users to make informed decisions.
• Data Engineering Support: Assist in the design, construction, and optimization of data pipelines, databases, and data models to ensure seamless data flow across systems.
• Building & Managing Data Pipelines: Develop and maintain robust data pipelines to collect, process, and integrate data from various sources. Ensure pipelines are optimized for performance, scalability, and reliability.
• Data Conditioning: Perform data cleansing, validation, transformation, and enrichment to ensure data quality and readiness for analysis and reporting. Manage large datasets and handle missing or inconsistent data.
• Data Analysis: Conduct data analysis to derive actionable insights, create reports, and support business teams with data-driven recommendations. Leverage statistical analysis to produce adhoc reports.
• Collaboration and Client Facing: Work closely with cross-functional teams, including data scientists and engineers, analysts, and business stakeholders to understand requirements and deliver solutions. Provide data driven business decisions, understand customer needs, design innovative solutions to inject efficiency and automation into business processes.
• Troubleshooting & Optimization: Identify and resolve performance issues, optimize data systems and processes for efficiency, and ensure that automated systems and data pipelines are running smoothly.
• Documentation & Reporting: Document development processes, RPA workflows, data pipelines, and analysis results clearly and effectively to support knowledge sharing and compliance.
• Data Quality & Governance: Ensure data is accurate, reliable, and compliant with data governance standards by working with engineering teams to implement proper validation, error-handling, and security practices.