IN_Senior Manager_Data Engineering (Azure/AWS/GCP/Oracle)_OC-Data & Analytics AITH_Advisory_Bhubaneswar

PwC; Khordha; On-site; INR 3,000,000 - 5,000,000; Full time

Job summary

Responsibilities include end-to-end data pipelines, data governance, and cloud migration initiatives, plus driving Agile delivery and adopting automation with Airflow, Jenkins, and Terraform. You will partner with enterprise architects and business stakeholders to translate requirements into resilient solutions.

Qualifications

• Experience designing data workloads across cloud platforms.
• Strong ETL/ELT pipeline knowledge and data processing.
• Experience with data lakes, data warehouses, lakehouses.
• Proficient in Python, SQL, Scala or Java.
• Experience with Spark, Kafka, Airflow.
• Familiar with Docker and Kubernetes in data workflows.
• Knowledge of Terraform, CloudFormation, ARM templates.

Responsibilities

• Lead data engineering teams to build scalable data platforms on cloud.
• Own end-to-end data ingestion, transformation, storage and delivery.
• Collaborate with architects, data scientists, analysts, and IT teams.
• Define standards for cloud-native data architectures and security.
• Oversee cloud migrations from on-prem to cloud.
• Implement data governance and data quality measures.
• Lead Agile delivery with sprint planning and stakeholder communication.
• Evaluate automation and orchestration tools like Airflow and Jenkins.

Skills

ETL/ELT pipelines

Data ingestion

Data transformation

Data lakes

Data warehouses

Lakehouses

Python

SQL

Scala/Java

Apache Spark

Kafka

Apache Airflow

Docker

Kubernetes

Terraform

Cloud automation

Tools

Azure Data Factory

Synapse Analytics

Databricks

AWS Glue

Redshift

BigQuery

Dataflow

Dataproc

Autonomous Database

Data Integration

Job description

Line of Service

Advisory

Industry/Sector

Not Applicable

Specialism

Data, Analytics & AI

Management Level

Senior Manager

Job Description & Summary

At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth.

In data engineering at PwC, you will focus on designing and building data infrastructure and systems to enable efficient data processing and analysis. You will be responsible for developing and implementing data pipelines, data integration, and data transformation solutions.

Why PWC

At PwC, you will be part of a vibrant community of solvers that leads with trust and creates distinctive outcomes for our clients and communities. This purpose-led and values-driven work, powered by technology in an environment that drives innovation, will enable you to make a tangible impact in the real world. We reward your contributions, support your wellbeing, and offer inclusive benefits, flexibility programmes and mentorship that will help you thrive in work and life. Together, we grow, learn, care, collaborate, and create a future of infinite experiences for each other. Learn more about us.

At PwC, we believe in providing equal employment opportunities, without any discrimination on the grounds of gender, ethnic background, age, disability, marital status, sexual orientation, pregnancy, gender identity or expression, religion or other beliefs, perceived differences and status protected by law. We strive to create an environment where each one of our people can bring their true selves and contribute to their personal growth and the firm's growth. To enable this, we have zero tolerance for any discrimination and harassment based on the above considerations.

Job Description & Summary

We are looking for a Senior Manager - Data Engineering to lead and manage data engineering teams responsible for building robust, scalable, and secure data infrastructure leveraging cloud technologies across Azure, AWS, Google Cloud Platform (GCP), Informatica and Oracle Cloud. The ideal candidate will combine strong technical expertise with leadership skills to drive data platform initiatives, optimize data pipelines, and collaborate cross-functionally to support analytics, BI, and data science needs.

Responsibilities

• Lead and mentor data engineering teams to design, build, and maintain high-performance, scalable data platforms on cloud environments including Azure, AWS, Google Cloud Platform (GCP), Informatica and Oracle Cloud
• Own the end-to-end lifecycle of data engineering solutions: data ingestion, transformation, storage, and delivery.
• Collaborate with enterprise architects, data scientists, business analysts, and IT teams to translate business requirements into scalable data engineering solutions.
• Drive adoption of best practices in cloud-native data architectures such as data lakes, data warehouses, and lakehouses.
• Define technical standards, architecture guidelines, and operational procedures to ensure reliability, performance, and data security.
• Oversee cloud migration projects and upgrade efforts for existing on-premises data platforms to cloud.
• Manage vendor relationships and evaluate emerging cloud services and tools relevant to data engineering.
• Implement and enforce data governance, data quality, and compliance measures.
• Monitor data pipeline performance and implement tuning, troubleshooting, and capacity planning.
• Lead Agile delivery processes with efficient sprint planning, backlog grooming, and stakeholder communication.
• Provide technical leadership in evaluating and incorporating automation and orchestration tools (e.g., Apache Airflow, Jenkins).
• Report progress, risks, and KPIs to senior leadership and stakeholders.

Mandatory skill sets

• Cloud Platforms Expertise:
• Demonstrated hands-on experience designing and managing data workloads across one or more cloud platforms:
• Microsoft Azure (Data Factory, Synapse Analytics, Databricks)
• AWS (Glue, Redshift, Athena, EMR)
• Google Cloud Platform (BigQuery, Dataflow, Dataproc)
• Oracle Cloud Infrastructure (Autonomous Database, Data Integration, Object Storage)
• Experience in cloud migration from traditional on-prem data systems to cloud-native data platforms.
• Data Engineering Fundamentals:
• Deep understanding of ETL/ELT pipelines, data ingestion, batch and streaming data processing.
• Expertise in data storage solutions: Data Lakes, Data Warehouses, Lakehouses, and relational/non-relational databases.
• Skilled in programming/scripting languages (Python, SQL, Scala, or Java).
• Experience with big data frameworks and tools such as Apache Spark, Kafka, and Apache Airflow.
• Familiar with containerization (Docker) and orchestration (Kubernetes) in data workflows.
• Knowledge of infrastructure automation tools (Terraform, CloudFormation, ARM templa