Data Engineer - PowerBI

Accountabilities:: Design, develop, and maintain scalable ETL/ELT pipelines capable of processing large volumes of structured and unstructured data. Build and optimize data warehouse, data lake, and lakehouse solutions to support efficient reporting and analytics. Integrate data from internal and external sources to create unified, reliable, and analysis-ready datasets. Implement data validation, cleansing, transformation, and reconciliation processes to maintain data accuracy, consistency, and completeness. Develop and maintain Power BI dashboards and reports for business and leadership stakeholders. Design optimized Power BI semantic models using star and snowflake schemas, with a focus on scalability and performance. Develop and optimize DAX measures, calculated columns, and KPIs aligned with business metrics. Use Power Query (M) to perform transformations and prepare reliable, refresh-ready reporting datasets. Manage Power BI Service deployments, including scheduled refreshes, workspace access, report sharing, and related administration. Improve report and dashboard performance through optimized data models, relationships, aggregations, and queries. Collaborate with product managers and business stakeholders to understand reporting requirements and translate them into technical deliverables. Provide technical recommendations that improve reporting accuracy, usability, performance, and decision-making. Participate in Agile ceremonies including sprint planning, daily stand-ups, reviews, and related delivery activities. Test pipelines, transformations, semantic models, dashboards, and end-to-end reporting workflows to ensure reliability and correctness. Develop appropriate unit tests and data validations while proactively identifying and resolving defects and performance bottlenecks. Continuously evaluate data engineering and Power BI technologies and recommend improvements to scalability, maintainability, and efficiency. Contribute to modern cloud-based data solutions using platforms such as Azure, AWS, and GCP. Requirements: Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field. At least 3 years of professional experience as a Data Engineer, BI Engineer, or in a closely related data and analytics role. Strong hands-on expertise with Power BI , including semantic data modeling, DAX, Power Query (M), dashboard development, and report optimization. Strong SQL skills, preferably with MySQL or PostgreSQL , including experience with query optimization. Proficiency in Python for data transformation, automation, integrations, and data-processing workflows. Solid understanding of data warehousing, lakehouse principles, data modeling, and performance-tuning concepts. Exposure to modern data technologies such as Databricks, Spark, PySpark, and Pandas . Experience working with APIs and data integrations, including frameworks such as Flask or similar technologies. Familiarity with development and data-engineering utilities such as logging, requests, subprocess, regular expressions, and pytest. Working knowledge of Git and modern collaborative software-development workflows. Comfortable working in Linux environments and writing basic shell scripts. Strong analytical and problem-solving abilities, with excellent attention to detail. Ability to investigate complex data issues and develop reliable, maintainable solutions. Strong communication skills and the ability to collaborate effectively with technical and non-technical stakeholders. Adaptability and willingness to learn new tools, technologies, and data-engineering practices as project requirements evolve. Ability to work effectively in an Agile environment while managing priorities, dependencies, and delivery expectations. Benefits: Competitive compensation based on experience, expertise, and skills. Fully remote work arrangement within India. Opportunity to work on impactful data engineering and business intelligence projects. Hands-on exposure to Power BI, modern data platforms, cloud technologies, and data engineering practices. Opportunities to work with technologies including Databricks, Spark, PySpark, Python, SQL, and cloud platforms. Collaborative environment involving data, engineering, product, and business stakeholders. Opportunities for continuous learning and professional development. Exposure to complex data challenges and opportunities to expand technical expertise across data engineering and analytics. Inclusive work environment focused on collaboration, innovation, empowerment, leadership, teamwork, and excellence.