S&C Global Network - AI - Song - Growth Analytics- Associate Manager

Accenture in India; Gurugram District; On-site; INR 3,200,000 - 5,400,000; Full time

Job summary

Accenture Applied Intelligence seeks a Data Engineering Manager (Full Stack & Cloud) to define and deliver scalable cloud-native data platforms and end-to-end data products. The role combines frontend/back-end ownership with people and program management, translating business priorities into modern architectures and coaching teams to deliver reliable data and ML-ready services for analytics and AI.

You will own architecture across ingestion, processing, storage, APIs, and UIs, drive cost

Qualifications

• BTech/ME/MTech in engineering from reputed institutes.
• Advanced degrees in CS or statistics/econometrics/economics preferred.
• Strong cloud and data engineering foundation with modern APIs.

Responsibilities

• Own end-to-end data platform and full-stack data product roadmap.
• Translate business needs into scalable cloud architectures and data pipelines.
• Design batch and streaming pipelines, microservices, and feature stores.
• Ensure CI/CD, IaC, containers and orchestration for reliable deployments.
• Lead cost optimisation, security, governance and data quality initiatives.
• Collaborate with Product, Data Science, Analytics, and Engineering teams.
• Promote engineering best practices: reviews, tests, observability, postmortems.
• Mentor engineers and leads, manage resources across pods.
• Drive migration and modernization from on-prem to cloud where applicable.
• Maintain stakeholder communications on status, risks, and roadmaps.

Skills

Full Stack

Cloud

Data Platform

APIs

ML/GenAI

Education

B Tech/M Tech in Engineering

Masters/M Tech in Computer Science

Master in Statistics/Econometrics/Economics

Tools

Docker

Kubernetes

Airflow

ADF

Job description

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