Accenture Applied Intelligence practice help our clients grow their business in entirely new ways. Analytics enables our clients to achieve high performance through insights from data - insights that inform better decisions and strengthen customer relationships. From strategy to execution, Accenture works with organizations to develop analytic capabilities - from accessing and reporting on data to predictive modelling - to outperform the competition
As part of our Analytics practice, you will join a worldwide network of over 20,000 smart and driven colleagues experienced in leading statistical tools, methods and applications. From data to analytics and insights to actions, our forward-thinking consultants provide analytically-informed, issue-based insights at scale to help our clients improve outcomes and achieve high performance.
Manager in Applied Intelligence
A Data Engineering Manager(Full Stack & Cloud) defines and delivers scalable, secure cloud-native data platforms and end-to-end data products. They combine technical ownership across frontend/back-end/infra with people and program management — translating business priorities into architecture, driving cloud strategy, enforcing operational excellence, and coaching teams to deliver reliable data and ML-ready services that enable analytics and AI.
Duties and Responsibilities:
- Own the end-to-end data platform and full-stack data product roadmap: architecture, delivery, reliability, and cost-efficiency.
- Translate business requirements into scalable cloud architectures (data ingestion, processing, storage, APIs, and UIs) that serve analytics, reporting, and ML/GenAI needs.
- Drive design and implementation of batch and streaming pipelines, microservices, APIs, feature stores, vector DBs, and data stores (relational & NoSQL).
- Ensure resilient deployments via CI/CD, IaC, containerization (Docker/Kubernetes), serverless patterns, and orchestration (Airflow/ADF/other).
- Lead cloud cost optimisation, capacity planning, security, compliance, and data governance (lineage, cataloguing, access controls).
- Partner with Product, Data Science, Analytics, and Engineering to prioritise platform features, SLAs, and SLOs.
- Establish and enforce engineering best practices: code reviews, testing, observability, incident runbooks, and postmortems.
- Hire, mentor, and grow engineers and leads; allocate resources, set goals, and manage delivery across multiple pods.
- Drive migration/modernization initiatives (on-prem → cloud, monolith → microservices, legacy ETL → lakehouse) when applicable.
- Maintain stakeholder communication: status reporting, technical tradeoffs, risk mitigation, and roadmap alignment.
Educational Background/qualifications
- B Tech/M Tech from reputed engineering colleges
- Masters/M Tech in Computer Science
- Master degree in Statistics/Econometrics/ Economics from reputed institute
