Sr. Data Engineer

Bharti Airtel · Mobile Networks · Broadband · SQL · 1 month ago

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Salary

≈ $20k – $44k per year (Estimated)

Location

In office (Delhi)

Seniority

Senior

First seen by Alion on Sep 9, 2026.

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Bharti Airtel

Bharti Airtel is a global telecommunications services provider that offers mobile network coverage, high-speed fiber broadband, and digital television solutions. Headquartered in New Delhi, India, the enterprise operates across numerous countries in South Asia and Africa, serving hundreds of millions of customers. Its extensive portfolio also includes ICT enterprise services, cloud infrastructure, submarine cable networks, and digital financial solutions.

airtel.in · HQ: New Delhi, India · Mobile Networks · Broadband · Est. 1995

Tech stack of Bharti Airtel

We are looking for a hands-on Senior Data Engineer to design and build scalable data solutions and reusable data platform capabilities on Google Cloud Platform (GCP). This role combines data engineering and data platform engineering. You will provide technical leadership for complex data-processing capabilities while remaining actively involved in architecture, design, and implementation. You will help define how data products are transformed, orchestrated, governed, and operated at scale, with a strong focus on reusability, automation, performance, reliability, and engineering standards.

Responsibilities:

Own the technical design and architecture of scalable data-processing and transformation solutions on GCP.
Design and build production-grade batch, incremental, and streaming data pipelines.
Develop and optimize complex transformations using BigQuery, SQL, and Dataform/dbt.
Build reusable, metadata-driven, and configuration-driven frameworks that reduce bespoke pipeline development.
Design orchestration patterns using Cloud Composer / Apache Airflow, including dynamic workflows, dependencies, retries, and recovery.
Develop distributed and streaming processing solutions using Dataflow / Apache Beam and Dataproc / Spark.
Define standards for data modeling, schema design, schema evolution, and data contracts.
Design and evolve modern analytical storage patterns, including Apache Iceberg.
Establish engineering patterns for data quality, reconciliation, metadata, lineage, and observability.
Optimize large-scale workloads for performance, scalability, latency, and GCP cost.
Define reusable engineering standards for testing, versioning, CI/CD, and production readiness.

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