Data Quality Analyst

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.

 Knowledge/

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.

Role: Data Analyst

Industry Type: Industrial Equipment / Machinery

Department: Data Science & Analytics

Employment Type: Full Time, Permanent

Role Category: Business Intelligence & Analytics

Education

UG: Any Graduate

PG: Any Postgraduate