📍 Job Overview

Job Title: Senior Associate - Cloud Data Engineer (Data & Analytics Advisory)
Company: PwC
Location: Bengaluru Millenia, India
Job Type: Full-Time
Category: Data Engineering
Date Posted: April 10, 2025
Experience Level: 5-10 years
Remote Status: On-site

🚀 Role Summary

Design, build, and maintain scalable data pipelines for various cloud platforms, including AWS, Azure, Databricks, and GCP.
Implement data ingestion and transformation processes to facilitate efficient data warehousing.
Optimize Spark job performance and stay proactive in learning and implementing new technologies.
Collaborate with cross-functional teams to deliver robust data solutions.

📝 Enhancement Note: This role requires a strong focus on cloud environments and data processing frameworks, with a significant emphasis on Spark and cloud services.

💻 Primary Responsibilities

Cloud Data Pipeline Development: Design, build, and maintain scalable data pipelines for AWS, Azure, Databricks, and GCP.
- AWS: Glue, Athena, Lambda, Redshift, Step Functions, DynamoDB, SNS.
- Azure: Data Factory, Synapse Analytics, Functions, Cosmos DB, Event Grid, Logic Apps, Service Bus.
- GCP: Dataflow, BigQuery, DataProc, Cloud Functions, Bigtable, Pub/Sub, Data Fusion.
Data Ingestion & Transformation: Implement data ingestion and transformation processes using cloud services to facilitate efficient data warehousing.
Spark Job Optimization: Optimize Spark job performance to ensure high efficiency and reliability.
Collaboration: Work with cross-functional teams to deliver robust data solutions and stay proactive in learning and implementing new technologies.
Real-time Data Processing: Work on Spark Streaming for real-time data processing as necessary.

📝 Enhancement Note: The primary responsibilities of this role revolve around designing, building, and maintaining data pipelines, optimizing Spark jobs, and collaborating with cross-functional teams to deliver data solutions in a cloud environment.

🎓 Skills & Qualifications

Education:

Master of Engineering, Bachelor of Technology, Bachelor of Engineering, Master of Business Administration, or Master of Computer Applications (MCA)

Experience:

4-7 years of experience in data engineering with a strong focus on cloud environments.

Required Skills:

Proficiency in PySpark or Spark (mandatory)
Proven experience with data ingestion, transformation, and data warehousing.
In-depth knowledge and hands-on experience with cloud services (AWS, Azure, or GCP).
Demonstrated ability in performance optimization of Spark jobs.
Strong problem-solving skills and the ability to work independently as well as in a team.

Preferred Skills:

Cloud Certification (AWS, Azure, or GCP) is a plus.
Familiarity with Spark Streaming is a bonus.

Mandatory Skill Sets:

Python, PySpark, SQL with (AWS or Azure or GCP)

Preferred Skill Sets:

Python, PySpark, SQL with (AWS or Azure or GCP)

📝 Enhancement Note: The required and preferred skills for this role emphasize proficiency in PySpark or Spark, experience with cloud services, and a strong focus on cloud environments. Additionally, the role requires strong problem-solving skills and the ability to work independently and in a team.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Demonstrate experience in designing, building, and maintaining scalable data pipelines for various cloud platforms.
Showcase projects that involve data ingestion, transformation, and warehousing using cloud services.
Highlight Spark job optimization and real-time data processing projects.

Technical Documentation:

Document code quality, commenting, and documentation standards for data engineering projects.
Include version control, deployment processes, and server configuration details.
Showcase testing methodologies, performance metrics, and optimization techniques used in data engineering projects.

📝 Enhancement Note: The portfolio and project requirements for this role focus on demonstrating experience in data pipeline development, data transformation, and Spark job optimization using cloud services. Additionally, technical documentation should include code quality, deployment processes, and performance metrics.

💵 Compensation & Benefits

Salary Range: INR 1,200,000 - INR 1,800,000 per annum (Based on experience and market standards for Senior Data Engineers in Bengaluru)

Benefits:

Competitive salary package
Performance-based incentives
Health, dental, and vision insurance
Retirement savings plans
Employee assistance programs
Learning and development opportunities
Flexible work arrangements

Working Hours: 40 hours per week, with additional hours as needed to meet project deadlines.

📝 Enhancement Note: The salary range for this role is estimated based on market standards for Senior Data Engineers in Bengaluru, India. The benefits package includes competitive compensation, performance-based incentives, health insurance, retirement savings plans, employee assistance programs, learning and development opportunities, and flexible work arrangements.

🎯 Team & Company Context

Company Culture:

PwC is a multinational professional services network with a strong focus on data, analytics, and AI.
The company values innovation, collaboration, and delivering high-quality services to clients.
PwC offers a vibrant community of solvers that lead with trust and create distinctive outcomes for clients and communities.

Team Structure:

The data and analytics team at PwC consists of data engineers, data scientists, and data analysts working together to deliver data-driven solutions to clients.
The team follows an Agile/Scrum methodology for project management and collaboration.

Development Methodology:

PwC uses Agile/Scrum methodologies for project management, with a focus on iterative development and continuous improvement.
The team follows best practices for code review, testing, and quality assurance.
Deployment strategies include CI/CD pipelines and automated deployment processes.

Company Website: https://www.pwc.in/

📝 Enhancement Note: PwC is a multinational professional services network with a strong focus on data, analytics, and AI. The company values innovation, collaboration, and delivering high-quality services to clients. The data and analytics team at PwC consists of data engineers, data scientists, and data analysts working together to deliver data-driven solutions to clients, following an Agile/Scrum methodology for project management and collaboration.

📈 Career & Growth Analysis

Web Technology Career Level: Senior Associate - Cloud Data Engineer (Data & Analytics Advisory)

Responsible for designing, building, and maintaining scalable data pipelines for various cloud platforms.
Collaborates with cross-functional teams to deliver robust data solutions.
Provides technical expertise and mentorship to junior team members.

Reporting Structure: Reports directly to the Data & Analytics Advisory Manager or equivalent.

Technical Impact: Has a significant impact on data processing, data warehousing, and data-driven insights for clients.

Growth Opportunities:

Technical Growth: Develop expertise in emerging cloud technologies, data processing frameworks, and data engineering best practices.
Leadership Growth: Gain experience in team management, project leadership, and architecture decision-making.
Career Progression: Progress to roles such as Senior Manager, Director, or Partner within the Data & Analytics Advisory practice.

📝 Enhancement Note: The career and growth analysis for this role highlights the technical impact of the position and the opportunities for growth in technical expertise, leadership, and career progression within the Data & Analytics Advisory practice at PwC.

🌐 Work Environment

Office Type: Modern, collaborative workspace with a focus on innovation and technology.

Office Location(s): Bengaluru Millenia, India

Workspace Context:

Collaborative Environment: The workspace encourages collaboration and interaction between data engineers, data scientists, and data analysts.
Development Tools: Access to modern development tools, multiple monitors, and testing devices.
Cross-functional Collaboration: Opportunities for cross-functional collaboration with designers, marketers, and other stakeholders.

Work Schedule: Standard full-time work schedule, with additional hours as needed to meet project deadlines.

📝 Enhancement Note: The work environment at PwC is modern, collaborative, and focused on innovation and technology. The workspace encourages collaboration and interaction between team members and offers opportunities for cross-functional collaboration with other stakeholders.

📄 Application & Technical Interview Process

Interview Process:

Technical Assessment: A hands-on technical assessment focusing on data pipeline development, data transformation, and Spark job optimization using cloud services.
Behavioral Interview: An interview focused on problem-solving, collaboration, and adaptability in a team environment.
Final Interview: A final interview with the hiring manager or a panel of senior team members to assess cultural fit and technical expertise.

Portfolio Review Tips:

Highlight projects that demonstrate experience in data pipeline development, data transformation, and Spark job optimization using cloud services.
Include case studies that showcase the impact of your work on data-driven insights and business outcomes.
Be prepared to discuss your approach to data quality, performance optimization, and user experience design.

Technical Challenge Preparation:

Brush up on your knowledge of cloud services (AWS, Azure, GCP), PySpark, and Spark.
Practice data pipeline development, data transformation, and Spark job optimization exercises.
Familiarize yourself with the latest trends and best practices in data engineering and cloud technologies.

ATS Keywords: [A comprehensive list of web development and server administration-relevant keywords for resume optimization, organized by category: programming languages, web frameworks, server technologies, databases, tools, methodologies, soft skills, industry terms]

📝 Enhancement Note: The application and technical interview process for this role focuses on assessing technical expertise in data pipeline development, data transformation, and Spark job optimization using cloud services. The portfolio review tips and technical challenge preparation guidance help candidates highlight their relevant experience and demonstrate their technical skills.

🛠 Technology Stack & Web Infrastructure

Cloud Platforms:

AWS: Glue, Athena, Lambda, Redshift, Step Functions, DynamoDB, SNS
Azure: Data Factory, Synapse Analytics, Functions, Cosmos DB, Event Grid, Logic Apps, Service Bus
GCP: Dataflow, BigQuery, DataProc, Cloud Functions, Bigtable, Pub/Sub, Data Fusion

Data Processing Frameworks:

PySpark or Spark (mandatory)

Data Warehousing:

AWS: Redshift, Glue
Azure: Synapse Analytics, Cosmos DB
GCP: BigQuery, DataProc

Real-time Data Processing:

Spark Streaming

📝 Enhancement Note: The technology stack for this role includes various cloud platforms (AWS, Azure, GCP), data processing frameworks (PySpark or Spark), data warehousing solutions, and real-time data processing tools. Familiarity with these technologies is essential for success in this role.

👥 Team Culture & Values

Data Engineering Values:

Innovation: Embrace new technologies and approaches to enhance data processing capabilities.
Collaboration: Work closely with cross-functional teams to deliver robust data solutions.
Performance Optimization: Continuously optimize data processing frameworks to ensure high efficiency and reliability.
User-Centric Design: Focus on delivering data-driven insights that meet user needs and drive business growth.

Collaboration Style:

Cross-functional Integration: Collaborate with designers, marketers, and other stakeholders to ensure data solutions meet user needs and business objectives.
Code Review Culture: Participate in code reviews to maintain high-quality standards and share knowledge with team members.
Knowledge Sharing: Actively share knowledge and expertise with team members to foster a culture of continuous learning and improvement.

📝 Enhancement Note: The data engineering values and collaboration style for this role emphasize innovation, collaboration, performance optimization, and user-centric design. The team culture encourages cross-functional integration, code review, and knowledge sharing to foster a culture of continuous learning and improvement.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Cloud Platform Complexity: Navigate the complexities of multiple cloud platforms (AWS, Azure, GCP) to design, build, and maintain scalable data pipelines.
Data Volume & Velocity: Manage high volumes of data with varying velocities and ensure efficient data processing and warehousing.
Data Quality & Consistency: Maintain data quality and consistency across multiple data sources and cloud platforms.
Real-time Data Processing: Develop and optimize real-time data processing solutions using Spark Streaming.

Learning & Development Opportunities:

Emerging Technologies: Stay up-to-date with the latest cloud technologies, data processing frameworks, and data engineering best practices.
Conferences & Certifications: Attend industry conferences, obtain relevant certifications (AWS, Azure, GCP), and engage with online communities to expand your knowledge and skills.
Mentorship & Leadership: Seek mentorship opportunities from senior team members and develop leadership skills through team management and architecture decision-making.

📝 Enhancement Note: The technical challenges for this role involve navigating the complexities of multiple cloud platforms, managing high volumes of data, maintaining data quality, and developing real-time data processing solutions. The learning and development opportunities focus on staying up-to-date with emerging technologies, attending industry conferences, and seeking mentorship and leadership development.

💡 Interview Preparation

Technical Questions:

Cloud Platforms: Be prepared to discuss your experience with AWS, Azure, and GCP, and how you have leveraged their services to design, build, and maintain scalable data pipelines.
Data Processing Frameworks: Demonstrate your proficiency in PySpark or Spark, and how you have optimized Spark jobs to ensure high efficiency and reliability.
Data Warehousing: Showcase your experience with data warehousing solutions (Redshift, Glue, Synapse Analytics, BigQuery, DataProc) and how you have used them to facilitate efficient data warehousing.

Company & Culture Questions:

Data Engineering Values: Demonstrate your understanding of PwC's data engineering values, including innovation, collaboration, performance optimization, and user-centric design.
Team Collaboration: Describe your experience working with cross-functional teams and how you have collaborated with designers, marketers, and other stakeholders to deliver robust data solutions.
Problem-Solving: Provide examples of how you have approached and solved complex data engineering challenges, and how you have optimized data processing frameworks to ensure high efficiency and reliability.

Portfolio Presentation Strategy:

Cloud Platform Projects: Highlight projects that demonstrate your experience with AWS, Azure, and GCP, and how you have leveraged their services to design, build, and maintain scalable data pipelines.
Data Processing Framework Projects: Showcase your proficiency in PySpark or Spark, and how you have optimized Spark jobs to ensure high efficiency and reliability.
Data Warehousing Projects: Present projects that demonstrate your experience with data warehousing solutions and how you have used them to facilitate efficient data warehousing.

📝 Enhancement Note: The interview preparation for this role focuses on assessing technical expertise in cloud platforms, data processing frameworks, and data warehousing. The company and culture questions evaluate the candidate's understanding of PwC's data engineering values, team collaboration, and problem-solving skills. The portfolio presentation strategy emphasizes projects that demonstrate experience with cloud platforms, data processing frameworks, and data warehaging.

📌 Application Steps

To apply for this Senior Associate - Cloud Data Engineer (Data & Analytics Advisory) position at PwC:

Tailor Your Resume: Highlight your relevant experience in data engineering, cloud platforms, and data processing frameworks. Include any certifications (AWS, Azure, GCP) and industry-specific keywords to optimize your resume for the ATS.
Prepare Your Portfolio: Showcase your projects that demonstrate experience in data pipeline development, data transformation, and Spark job optimization using cloud services. Include case studies that highlight the impact of your work on data-driven insights and business outcomes.
Practice Technical Exercises: Brush up on your knowledge of cloud services (AWS, Azure, GCP), PySpark, and Spark. Practice data pipeline development, data transformation, and Spark job optimization exercises to prepare for the technical assessment.
Research PwC: Familiarize yourself with PwC's data engineering values, team culture, and company mission. Be prepared to discuss how your skills and experience align with PwC's focus on innovation, collaboration, and delivering high-quality services to clients.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and web development/server administration industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

IN-Senior Associate_Cloud Data Engineer_Data and Analytics_Advisory_PAN India