Job
Description
As part of the operational arm of data analytics, the Analytics Analyst - Quality will support the implementation of data governance decisions, maintain data quality documentation, and train end users on analytics solutions. The role involves developing and maintaining low-code applications, integrating data sources, and facilitating data governance processes to ensure the delivery of high-quality insights for business decision-making. Key Responsibilities: Develop & Maintain Low-Code Applications: Design, develop, and enhance low-code applications using Mendix/Power Apps based on business requirements. Integrate with Data Sources: Connect applications to SQL databases, APIs, and other data sources for seamless data flow and business logic implementation. UI/UX Implementation: Create intuitive, responsive, and user-friendly interfaces ensuring a smooth end-user experience. Power BI Integration and Development: Embed Power BI dashboards within applications for real-time reporting and analytics. Performance Optimization: Write efficient queries and optimize application performance for scalability and security. Troubleshooting & Debugging: Identify, diagnose, and resolve technical issues in applications and ensure smooth deployment. Collaboration & Documentation: Work closely with business analysts, stakeholders, and cross-functional teams to gather requirements, document solutions, and provide technical guidance. Support Data Governance Processes: Facilitate intake processes to reduce rework and promote reuse of analytics solutions. Data Integration and Cleansing: Collaborate with peers to integrate data from warehouses, data lakes, and other source systems. Cleanse data to ensure report accuracy and reduce duplication, maintaining documentation of the cleansing process. Support Analytics Projects: Produce insights to improve business decision-making and deliver results using reports, business intelligence technology, or other appropriate mechanisms. Enforce Data Standards: Assist in establishing and enforcing guidelines and standards for data collection, integration, and processes. Data Ingestion: Support project work to ingest key data into the data lake, ensuring the creation and maintenance of relevant metadata and data profiles. Stakeholder Support: Assist stakeholders and business teams with finding necessary and relevant data. Promote Analytics Best Practices: Participate in relevant communities of practice and coach business users in developing capabilities within the analytics ecosystem. Communication Preparation: Assist with the preparation of communications to leaders and stakeholders. Qualifications: Education: B. Tech in Computer Science (CS), Electrical & Electronics Engineering (EEE), Information Technology (IT), or a related field. Experience: Minimum 3 to 4 years of hands-on experience in Mendix/Power Apps, Power BI, and SQL. Key Skills: Mendix/Power Apps Power BI SQL Power Automate GITLAB/GITHUB Competencies: 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 to extract meaningful patterns and business insights. Data Mining: Identifying relationships and patterns through data exploration and visualization techniques. Data Modeling: Creating, writing, and testing data models to meet business, technical, and compliance requirements. Data Communication and Visualization: Illustrating data visually to construct a narrative around business problems and solutions. Data Literacy: Expressing data in context and describing the use-case application and resulting value. Data Profiling: Assessing data issues and cleansing requirements to establish high-quality data. Data Quality: Identifying and correcting flaws in data to support effective information governance and decision-making. Values Differences: Recognizing the value that different perspectives and cultures bring to an organization.
