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Mid-level metro role, popular Senior Data Engineer title, and broad required tech stack drive high competition.
Core data engineering skills are broadly transferable across industries despite telecom domain preference.
Explicit 6+ years plus mandatory skills like Spark, Kafka, cloud, and Kubernetes make shortlisting highly strict.
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Lead architecture, design, development, and support of scalable, cloud-native data platforms and pipelines including batch and streaming data processing.
Own end-to-end lifecycle of data engineering initiatives from solution design through deployment, performance tuning, and production support.
Collaborate cross-functionally with product, analytics, and engineering teams to deliver reliable data solutions supporting multi-market business operations and analytics.
6+ years of data engineering or big data platform development experience with enterprise-scale delivery.
Strong skills in ETL/ELT pipeline development using Python, Spark/PySpark, and SQL.
Experience with at least one modern data lakehouse or warehousing technology (Apache Iceberg, Hive, Trino, Big Query, Redshift, ClickHouse, or Snowflake).
Experience with real-time pipelines (e.g. Apache Kafka) and cloud object storage (Amazon S3 or compatible).
Experienced in designing and optimizing scalable data models and pipelines integrating relational and NoSQL databases.
Skilled in deploying and managing data applications on Kubernetes and using CI/CD tools like GitLab pipelines.
Comfortable working in fast-paced, cross-functional environments supporting telecommunications or multi-market data platforms.