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Power BI Data Engineer — Enterprise Analytics

Job in 500001, Hyderabad, Telangana, India
Listing for: Accel4 Inc
Full Time position
Listed on 2026-02-04
Job specializations:
  • IT/Tech
    Data Engineer, Data Science Manager, Data Analyst, Business Systems/ Tech Analyst
Job Description & How to Apply Below
Power BI Data Engineer — Enterprise Analytics

Location:

Hyderabad, India (On-site)

About the Role

Design, develop, and maintain interactive Power BI dashboards and reports to empower business stakeholders with actionable insights.

Responsibilities

Build and optimize ETL pipelines for data ingestion, transformation, and loading into analytics platforms (Power BI, Snowflake, Databricks, or Microsoft Fabric).
Develop and implement star schemas, data models, and semantic models for analytics cubes to support enterprise reporting and BI needs.
Collaborate with data scientists, analysts, and business teams to translate requirements into scalable data solutions.
Ensure data quality, governance, and security across all BI workflows, adhering to best practices.
Deploy and monitor data solutions on cloud platforms (Azure preferred, AWS/GCP a plus).
Continuously improve performance of data pipelines and reports through optimization and automation.

Qualifications

5–7 years of experience in data engineering, with a focus on BI solutions and Power BI development.

Required Skills

Strong expertise in Power BI (DAX, Power Query, report design) and building intuitive, high-performance dashboards.
Proven experience designing and implementing ETL processes and star schemas for analytics cubes.
Hands-on experience with cloud data platforms like Snowflake, Databricks, or Microsoft Fabric.
Proficiency in SQL for querying and transforming large datasets.
Familiarity with Azure data services (e.g., Azure Data Factory, Synapse Analytics) or equivalent cloud tools.
Self-motivated with a proactive approach to problem-solving, capable of managing complex projects with minimal oversight.

Preferred Skills

Familiarity with data orchestration tools (e.g., Apache Airflow, Prefect).
Knowledge of data warehousing concepts and tools (e.g. Fabric, Redshift, Big Query).
Exposure to AI/ML integration for predictive analytics or anomaly detection.
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