Data Modeler - Telecom Domain

We are seeking an experienced Data Modeler with a strong telecom industry background to join our data engineering team. The ideal candidate will have deep domain knowledge in telecom business processes (BSS/OSS), data flows, and KPIs, and will be responsible for designing, developing, and maintaining logical and physical data models that support our enterprise data initiatives.

• Design, implement, and maintain conceptual, logical, and physical data models using industry best practices.
• Collaborate with business stakeholders and subject matter experts in the telecom domain to understand data requirements (e.g., customer data, CDRs, usage, network, billing, CRM).
• Analyze existing data systems and data flows to identify gaps, redundancies, and areas for improvement.
• Create and maintain data dictionaries, metadata repositories, and data lineage documentation.
• Work closely with ETL developers, data architects, and data analysts to ensure models are implemented as designed.
• Ensure data models are optimized for performance, maintainability, and scalability.
• Define and enforce data governance and data quality standards across telecom datasets.
• Support data warehouse and data lake design initiatives (e.g., Snowflake, Redshift, BigQuery).

• 4+ years of experience in data modeling
• Experience in telecom industry.
• Strong understanding of telecom data domains: Subscriber, Usage, Billing, Network, Products, Services, CRM, etc.
• Hands-on experience with data modeling tools such as Erwin, PowerDesigner, ER/Studio, or similar.
• Proficiency in SQL, data profiling, and metadata management.
• Experience with data warehousing concepts, dimensional modeling (Kimball/Inmon), and star/snowflake schemas.
• Familiarity with ETL processes and data integration tools (e.g., Informatica, Talend, Apache NiFi).
• Experience working in cloud data platforms (AWS, GCP, Azure) is a plus.
• Knowledge of BSS/OSS systems like Amdocs, Netcracker, Ericsson, etc., is highly desirable.

• Telecom certifications or training (e.g., TM Forum, eTOM, SID models).
• Experience with big data technologies (e.g., Spark, Hive, Kafka).
• Knowledge of data governance frameworks and tools (e.g., Collibra, Alation).
• Strong communication and stakeholder engagement skills.