Deutsche Telekom Digital Labs
Engineering Manager Data
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 recent 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 an Engineering Manager Data to join our Data team. This role suits someone with around 10-14 years of relevant experience who can lead engineers, own delivery and production outcomes, and build reliable, scalable data and backend platforms that power Deutsche Telekom's Future Marketing Platform (FMP).
Lead a high-performing team of engineers in designing, building and operating scalable backend services, distributed systems,



and reusable data pipelines and data products that support Deutsche Telekom's Future Marketing Platform (FMP).
Own end-to-end engineering delivery across design, development, deployment and production operations, with a focus on reliability, performance, security and business correctness. Build reusable platform capabilities for FMP use cases such as campaign configuration, cross-campaign processing, targeting and personalisation, consent-aware workflows and campaign dispatch, as applicable to your team's scope. Own the quality, reliability and cost of the data platforms your team runs, with clear SLAs, monitoring and observability. Apply capacity planning, performance profiling, load testing and cost-performance optimisation to keep them efficient as they scale.
Design pipelines and platforms on cloud data services with scalable ingestion, orchestration and transformation. Drive cloud-native engineering on AWS, using relevant compute, storage, messaging, container and managed database services, and apply sound data modelling, query optimisation and high-performance data access across relational and NoSQL databases.
Apply strong engineering fundamentals in Java and/or Python, microservices, REST APIs, concurrency, asynchronous processing, caching, consistency, fault tolerance, high availability and resilience patterns.
Set and uphold engineering standards for CI/CD, automated testing, deployment automation, infrastructure as code and security controls. Apply standards for data contracts, metadata, lineage, data quality and security across your pipelines,



including privacy and consent requirements.
Drive the pragmatic adoption of AI-enabled capabilities, including LLM integration and workflow automation, to improve automation, engineering productivity and business workflows. Partner with data science, analytics and product teams to deliver trusted, AI-ready datasets.
Collaborate closely with Product, Architecture, Data Engineering, Platform Engineering, Security and Operations teams, including across distributed teams. Hire, coach and grow engineers through regular feedback, clear career paths and a culture of inclusion, accountability and continuous learning. Bring 10-14 years of software engineering experience, including substantial backend and distributed systems experience, and a track record of leading and developing engineering teams.
Familiarity with agentic workflows, model-serving platforms and managed AI services such as AWS Bedrock or Vertex AI, along with experience building AI-ready data platforms that support ML and GenAI workloads and AI-assisted software development practices.
Experience integrating backend services with analytical data platforms such as BigQuery or Spark, and familiarity with data contracts, schema governance and schema evolution.
Familiarity with domain-driven design, API lifecycle management and platform engineering principles, plus experience supporting marketing, campaign management, targeting, personalisation and consent-aware workflow or campaign dispatch platforms.
Background in telecom, fintech or large enterprise ecosystems, and experience managing global, distributed engineering teams.
A B.Tech or equivalent degree in a relevant field.
Build digital products from scratch on modern, cloud-native infrastructure.
A fast, agile culture where releases ship quickly and your judgment drives the roadmap.

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