Cisco – Data Engineer

Hyderabad
July 6, 2026
Full Time
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Job Description

  • Design, build, and maintain scalable and efficient data pipelines to ingest, process, and store large volumes of data from diverse sources.
  •  Creating and optimizing robust data models and architectures that support advanced analytics, reporting, and machine learning initiatives.
  • Write production-grade SQL and Python scripts for data transformation, pipeline automation, and integration with upstream and downstream systems
  • Instrument data pipelines with robust quality frameworks—including dbt tests, row count validation, null assertions, and referential integrity checks—to ensure metric reliability for executive reporting.
  • Contribute to AI integration workstreams, including building data tables and pipeline structures that support LLM-generated insight delivery.
  • Evaluate and adopt AI-native data tooling—including Snowflake Cortex, dbt Copilot, and related capabilities—in line with the team’s AI future-readiness direction set by VP leadership
  • Strong expertise in Snowflake data warehousing platform and DBT for data transformation and pipeline orchestration.
  • Proficiency in SQL for data querying, validation, and test case design across Snowflake and Teradata environments.
  • Experience with Python for scripting automation and implementing AI-driven data processes.

Minimum Qualifications

4+ years of professional experience in data engineering or analytics engineering, with demonstrated ownership of production-grade Snowflake environments including query optimization, RBAC configuration, and schema design.

Expert-level SQL, including window functions, recursive CTEs, complex multi-level aggregations, and query performance profiling in a cloud data warehouse environment.
Intermediate Python proficiency for data pipeline scripting, ETL/ELT automation, and lightweight data wrangling using pandas, numpy, or equivalent libraries.
Demonstrated experience designing data architecture that supports analytical reporting at enterprise scale—including dimensional modeling, object rationalization, and parametric configuration layer design.

Preferred Qualifications

Working familiarity with building and maintaining Snowflake, DBT -based pipeline
Experience incorporating AI outputs into data pipelines—including consuming LLM API responses as structured data, feature engineering for predictive models, or building tables that support AI summary generation workflows.
Exposure to Business Objects, Power BI etc semantic model consumption and ability to diagnose data-layer issues that surface as report-layer errors, enabling clean handoffs with BI engineering counterparts.
Experience with pipeline orchestration tools such as Airflow, Prefect, or dbt Cloud job scheduling, including DAG dependency management and pipeline health monitoring.
Git-based development discipline, including branch management, PR workflows, and CI/CD awareness applied to dbt or pipeline codebases; experience with data observability frameworks is a plus.

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