Data Quality Analyst

DESCRIPTION

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.

• B.Tech/BE in Computer Science, IT, or a relevant field.
• College, university, or equivalent degree in a relevant technical discipline, or equivalent experience required.

• Collaborates - Building partnerships and working collaboratively with others to meet shared objectives.
• Communicates effectively - Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences.
• Customer focus - Building strong customer relationships and delivering customer-centric solutions.
• Interpersonal savvy - Relating openly and comfortably with diverse groups of people.
• Data Analytics - Discovering, interpreting, and communicating qualitative and quantitative data; determining conclusions relying on knowledge of business or functional frameworks; simultaneously applying statistics, data validity, data visualization, and problem-solving approaches to effectively extract meaningful patterns and business insights; presenting conclusions and outcomes that enable data-driven business decisions.
• Data Mining - Extracting insights from data by identifying relationships and patterns through a suite of data exploration and data visualization techniques to understand the underlying structure of the data and enable sound conclusions upon model building.
• Data Modeling - Creating, writing, and testing data models, test scripts, and build scripts using industry standards and tools, version control, and build and test automation to meet business, technical, security, governance, and compliance requirements.
• Data Communication and Visualization - Constructing a tale of the business problem, root cause, solution options, and opportunities through illustrating data visually, including reports and dashboards.
• Data Governance - Assuring definition, ownership, metadata management, and security for Cummins data.
• Data Literacy - Expressing data in context, including data sources and constructs, analytical methods, and applied techniques; describing the use-case application and resulting value.
• Data Profiling - Assessing data issues and cleansing requirements to perform data extraction, mapping, collection, and testing; establishing good, quality data.
• Data Quality - Identifying, understanding, and correcting flaws in data that supports effective information governance across operational business processes and decision-making.
• Values differences - Recognizing the value that different perspectives and cultures bring to an organization.

• 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.

• 1-3 years of hands-on experience with Power BI, Power Apps, and SQL.