Data Analyst / Analytics Engineer (Databricks)

Company

hirify.global is recruiting for a multinational telecommunications technology company that transforms complex data into business insights and scalable analytics solutions.

What you will do

• Analyze and integrate data across applications, source systems, and business domains.
• Design, build, and maintain scalable data pipelines, transformation workflows, and curated analytical datasets.
• Use Databricks to process, transform, optimize, and operationalize large-scale datasets.
• Develop Power BI dashboards and reports, including semantic logic, data modeling, visualization, and executive reporting.
• Investigate relationships between customers, products, locations, revenue, operations, and business performance.
• Partner with stakeholders to define KPIs, solve business problems, investigate data quality issues, and implement sustainable fixes.

Requirements

• Strong SQL development and data analysis experience.
• Hands-on experience with Databricks for large-scale data processing, transformation, and analytics.
• Experience designing and maintaining data pipelines, transformation workflows, and curated analytical datasets.
• Strong Power BI experience covering dashboards, data modeling, visualization, and business reporting.
• Understanding of data lineage, data quality, transformation logic, scalable data modeling, and end-to-end analytics.
• Strong problem-solving, communication, stakeholder management, and data reconciliation skills.

Nice to have

• Experience with Snowflake, Python, Spark, Terraform, or enterprise data warehouse environments.
• Experience with modern cloud-based analytics platforms and data engineering frameworks.
• Knowledge of telecommunications, customer, revenue, operational, or financial data domains.
• Familiarity with KPI governance, executive reporting, and semantic layer design.

Culture & Benefits

• Work with cross-functional teams in a fast-paced environment.
• Support business and executive stakeholders through trusted, self-service analytics.
• Improve the performance, reliability, usability, documentation, and maintainability of analytical workflows.