Data engineer

• Design, develop, and maintain real-time and batch data pipelines leveraging Kafka, Spark, and Hadoop components.
• Must have understanding of Teradata CLDM, should know how to create new Data Model or modify existing data model based on business requirement
• Collaborate with business analysts and data architects to translate business requirements into robust data models and ETL frameworks.
• Apply Relational and Dimensional modeling techniques to design databases and ensure data is organized effectively for both operational and analytical purposes.
• Write, debug, and optimize SQL and Stored Procedures to ensure efficient data processing.
• Work closely with BI, Data Science, and Campaign teams to ensure seamless data availability for analytics
• Work closely with Data Architects, Analysts, and Business Stakeholders to translate business requirements into database solutions.
• Ensure all database design and code is well-documented and follows best practices for performance and maintainability.
• Involves designing fact and dimension tables for reporting and analytics purposes, often in a star or snowflake schema.
• Develop and maintain technical documentation (data flow diagrams, source-to-target mappings, architecture documents).
• Ensure that the Data Dictionary is always up-to-date, capturing all changes to the database schema, including newly created or modified tables, columns, views,
• Perform data quality checks, validation, and ensure end-to-end data accuracy and lineage.
• Support and troubleshoot real-time streaming jobs and ensure high availability of data pipelines

• Strong expertise in real-time data integration using Kafka, Spark Streaming, or Dataflow.
• Hands-on experience with Hadoop ecosystem components (HDFS, Hive, Sqoop, Spark etc.).
• Strong Data Modeling concepts including FSLDM, CLDM, and Dimensional / Data Vault modeling.
• Deep understanding of Telecom domain (BSS/OSS, CDR, usage, revenue, and campaign data).
• Experience building and optimizing ETL pipelines and data ingestion frameworks for structured and unstructured data.
• Proficiency in SQL and distributed data processing using Hive, Spark SQL, or PySpark.
• Good understanding of data governance, data quality, and lineage frameworks.
• Strong analytical and problem-solving skills.
• Excellent communication and collaboration skills with cross-functional teams.
• Experience in working on data vartulization tools like (Tibco. Trino etc)

• Familiarity with Data Catalogs, Metadata Management, and NDMO data governance standards.
• Experience with Data Catalogue tools.
• Familiarity with CI/CD pipelines, Git.
• Knowledge of ETL orchestration tools like Airflow, NiFi

Master Works