Line of Service

Advisory

Industry/Sector

Not Applicable

Specialism

Data, Analytics & AI

Management Level

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: A career within….

Responsibilities:

1.Design and build robust streaming and batch data pipelines with AWS Glue, Lambda, Kinesis Data Streams/Firehose, Amazon MSK (Kafka), and Step Functions.

2.Architect end-to-end systems for real-time ingestion, processing, and analytics, leveraging Kinesis, MSK, Managed Flink, EMR, and Lambda for low-latency, high-availability operations.

3.Implement batch ETL pipelines using Glue, EMR (Spark/Hadoop), and S3, optimizing for cost and performance while handling large-scale datasets.

4.Integrate source systems (IoT, logs, transactional DBs, third-party feeds) and downstream destinations (Redshift, S3, DynamoDB, OpenSearch, Aurora, etc.).

5.Design and implement architectures supporting event-driven microservices, streaming analytics, materialized views, and near-real-time dashboards.

6.Ensure scalability, durability, ordering, and fault-tolerance in stream storage, using Kinesis/MSK and S3 for replay, backfill, and recovery scenarios.

7.Standardize patterns for Change Data Capture (CDC), late/duplicate data handling, data enrichment, stream normalization, and schema evolution.

8.Govern data security (encryption, access control, network isolation), ensure compliance, and embed monitoring/observability pipelines for SLA/SLO adherence.

Required Experience and Skills

1.Experience in data engineering/architecture, including significant leadership in AWS cloud data platforms.

2.Expert in designing streaming architectures with Kinesis, Kafka (MSK), Managed Flink, Lambda, and Step Functions.

3.Strong proficiency in batch ETL (AWS Glue, EMR, Spark/Hadoop), large-scale data lake/lakehouse implementation (S3, Delta Lake, Redshift).

4.Hands-on experience architecting hybrid streaming/batch workflows for analytics, reporting, ML, and near-real-time applications.

5.Deep understanding of distributed computing, parallelization, and fault-tolerance strategies for high-velocity, high-volume data.

6.Familiarity with real-time dashboards, API publication (REST, WebSockets), and materialized views for user-facing analytics.

Proficient in network/security controls, IAM design, encryption (KMS), and data governance. Experience with DevOps/IaC (CloudFormation, Terraform).

7.Excellent communication and stakeholder leadership; ability to translate business requirements to technical solution patterns.

Mandatory skill sets:

AWS Data Architect

Preferred skill sets:

AWS Data Architect

Years of experience required:

8-12 Years

Education qualification:

B. Tech/MBA/MCA / M. Tech

Education (if blank, degree and/or field of study not specified)

Degrees/Field of Study required: Bachelor of Technology, MBA (Master of Business Administration), Bachelor of Engineering

Degrees/Field of Study preferred:

Certifications (if blank, certifications not specified)

Required Skills

AWS Architecture

Optional Skills

Accepting Feedback, Accepting Feedback, Active Listening, Agile Scalability, Amazon Web Services (AWS), Analytical Thinking, Apache Airflow, Apache Hadoop, Azure Data Factory, Coaching and Feedback, Communication, Creativity, Data Anonymization, Data Architecture, Database Administration, Database Management System (DBMS), Database Optimization, Database Security Best Practices, Databricks Unified Data Analytics Platform, Data Engineering, Data Engineering Platforms, Data Infrastructure, Data Integration, Data Lake, Data Modeling {+ 32 more}

Desired Languages (If blank, desired languages not specified)

Travel Requirements

Not Specified

Available for Work Visa Sponsorship?

Government Clearance Required?

Job Posting End Date

IN_Manager_AWS Data Architect_D&A_Advisory_PAN India

PWC

Job Description

About Company

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