Deutsche Telekom Digital Labs
Data Engineer -II
Data · Gurgaon · Full-time
Deutsche Telekom Digital Labs is the engineering hub of Deutsche Telekom, Europe’s #1 telecom brand. We build the digital products that millions of customers across Europe use every day. AI-first by design, we don't just adopt new technology, we build with it.
1 most valuable telecom brand in the world, and #11 across all brands (Brand Finance Global 500, 2026)
~ If you're someone who believes data is the most underutilised superpower in any organisation, you'll fit right in. This team turns raw signal into sharp decisions, obsesses over quality at the source, and never settles for a dashboard when a model can do more. We don't just analyse the past, we build what helps shape what comes next.
AI tools, on us — Every engineer ships with AI. Time to recharge — Uncapped leaves, birthday leave, flexible hours, and sabbatical options.
Family first — 9 months maternity on full pay, 15 days paternity, crèche support, infertility treatment and adoption leave.
Health & wellness — Medical cover up to ₹15L for you and your family, unlimited free doctor consults, 24/7 counselling and free Cult.Money & everyday — Flexi benefits for car, fuel and books. Free meals and snacks on campus. Monthly phone and broadband allowance, plus relocation support when you need it.
We're looking for a Data Engineer -II to join our Data team. In this role, you will design, build and operate reliable, scalable and cost-efficient data pipelines and data products on Google Cloud Platform. It suits someone with around 4-6 years of relevant experience who takes ownership of solutions end to end, from design through production support.
Design,



build and maintain production-grade batch and incremental ETL/ELT pipelines on Google Cloud Platform, drawing on 4+ years of hands-on data engineering experience and designing solutions independently rather than only implementing predefined specifications.
Develop complex, well-optimised transformations in BigQuery using advanced SQL (complex joins, CTEs, window functions and query tuning) and Dataform and/or dbt, and tune queries and pipelines for both performance and cloud cost at large scale.
Write clean, tested Python (with Scala or Java as alternatives) for data processing, automation and testing, and build distributed processing solutions with Dataflow / Apache Beam or Dataproc / Spark / PySpark, using Flink or Kafka where streaming is needed.
Build and maintain orchestrated workflows with Cloud Composer / Apache Airflow (or comparable tools such as Dagster), and create reusable transformation components, libraries, templates and metadata- or configuration-driven patterns that other teams can adopt.
Design data models, applying normalisation, denormalisation and dimensional modelling, that serve analytical and downstream data-product needs, and define and maintain schemas and data contracts while managing schema evolution safely.
Implement incremental and idempotent processing patterns,



along with automated data-quality validation, reconciliation, and metadata and lineage capture, so that data is trustworthy, auditable and discoverable.
Use Git, automated testing and CI/CD to ship changes safely, and build monitoring, alerting and operational controls (including runbooks and SLAs) for production pipelines.
Troubleshoot and support production pipelines, performing root-cause analysis and driving lasting fixes; apply data security, privacy and access-control practices appropriate to sensitive customer data.
Collaborate with Data Engineers, Platform Engineers, DevOps, Architects and business stakeholders to translate requirements into solutions, contribute to engineering standards and design reviews, and mentor junior engineers through code reviews and knowledge sharing.
Experience with Apache Iceberg or other up-to-date lakehouse table formats, and with streaming or event-driven processing, including Change Data Capture.
A practical understanding of data product and data mesh principles, including building reusable components used by multiple engineering teams.
Familiarity with Infrastructure as Code, such as Terraform, for provisioning and managing data platforms.
Experience with real-time feature stores and ML data pipelines, and with using AI-assisted tooling to improve data engineering productivity.
Build digital products from scratch on modern, cloud-native infrastructure.
A real mandate to run lean and AI-first, not lip service, but how we actually operate.
A fast, agile culture where releases ship quickly and your judgment drives the roadmap.

📌 Data Engineer SQL - Flexible Working (Gurugram)
🏢 Deutsche Telekom Digital Labs Private
📍 Gurugram