Data Quality Analyst

As a Data Analyst in the Data Science & Analytics department of an Industrial Equipment / Machinery company, you will be responsible for the following key responsibilities: - Serve as an expert on data quality tools. - Manage metadata and data standards to ensure adherence, working closely with IT. - Develop and maintain rules to support a robust data quality process. - Use Key Performance Indicators (KPIs) to measure data quality. - Assist with tracking, research, and analytical support; provide input to the design and development of project plans. - Maintain documentation, notes, databases, and other records; communicate the state of data quality. - Assist with managing project risk using quality tools to identify areas of risk. - Identify and track data quality issues; create and execute action plans for resolution. - Escalate issues and critical conflicts in a timely fashion. - Document and share team learnings with other teams; draw on other experiences to enhance the success of the project. - Help the business to prepare data. - Assist with initial data exploration steps (binning, pivoting, summarizing, and finding correlations). - Assist with cataloguing business attributes to enable data discovery. - Support business to establish and enforce guidelines for data collection, integration, and processes. - Perform data and systems analysis as required to support data governance processes. - Understand data governance roles and processes, and ensure they are followed. - Assist with preparation of communications to leaders and stakeholders. In addition to the above responsibilities, you are expected to have the following technical skills: Technical Skills: - Power BI: - Develop, design, and maintain interactive Power BI dashboards and reports. - Connect and integrate data from various sources like SQL databases, APIs, and cloud services. - Implement DAX functions for data transformation and advanced calculations. - Optimize Power BI performance and manage data refresh schedules. - Python: - Utilize Python libraries (such as Pandas, NumPy, Matplotlib, and Seaborn) for data manipulation and analysis. - Conduct exploratory data analysis (EDA) to uncover insights and trends. - Develop scripts for data cleaning, preparation, and visualization. - Use Python for automating data processing and analysis workflows. - Implement machine learning algorithms (supervised and unsupervised) using libraries like Scikit-learn. - Power Apps Development: - Design and develop custom Power Apps applications to meet business requirements. - Integrate Power Apps with Power Automate, SharePoint, and other Microsoft services. - Troubleshoot and optimize existing Power Apps solutions. - SQL Development & Database Management: - Write, optimize, and manage complex SQL queries, stored procedures, and views. - Ensure data integrity, performance tuning, and database security. - Work on ETL processes for data extraction, transformation, and loading. - Automation & Integration: - Leverage Power Automate to automate workflows and data processes. - Integrate Power BI and Power Apps with external systems. - Basic Statistics: - Perform statistical analysis and hypothesis testing. - Use statistical methods to interpret and analyze data trends. - Collaboration & Documentation: - Work closely with business stakeholders to gather and understand requirements. - Document data models, report structures, and Power Apps solutions. - Provide training and support to end users. Experience: - 1-3 years of hands-on experience with Power BI, Power Apps, and SQL. Please note that this role requires a candidate with at least a graduate degree. As a Data Analyst in the Data Science & Analytics department of an Industrial Equipment / Machinery company, you will be responsible for the following key responsibilities: - Serve as an expert on data quality tools. - Manage metadata and data standards to ensure adherence, working closely with IT. - Develop and maintain rules to support a robust data quality process. - Use Key Performance Indicators (KPIs) to measure data quality. - Assist with tracking, research, and analytical support; provide input to the design and development of project plans. - Maintain documentation, notes, databases, and other records; communicate the state of data quality. - Assist with managing project risk using quality tools to identify areas of risk. - Identify and track data quality issues; create and execute action plans for resolution. - Escalate issues and critical conflicts in a timely fashion. - Document and share team learnings with other teams; draw on other experiences to enhance the success of the project. - Help the business to prepare data. - Assist with initial data exploration steps (binning, pivoting, summarizing, and finding correlations). - Assist with cataloguing business attributes to enable data discovery. - Support business to establish and enforce guidelines for data co