📍 Job Overview

Job Title: Cloud Data Engineer (AWS)
Company: Barclays
Location: Gera Commerzone SEZ, Pune, India
Job Type: Full-time, On-site
Category: Data Engineering, Cloud Computing
Date Posted: 2025-08-08

🚀 Role Summary

Design and implement data architectures, pipelines, and warehouses to manage data volumes, velocity, and security.
Collaborate with data scientists to build and deploy machine learning models.
Ensure data accuracy, accessibility, and security through robust data management practices.
Leverage AWS cloud services, SQL, PySpark, and other relevant technologies to drive data-driven insights and innovation.

📝 Enhancement Note: This role requires a strong focus on data management, processing, and analysis, with a significant emphasis on AWS cloud services and machine learning integration.

💻 Primary Responsibilities

Data Architecture & Pipeline Development: Design, build, and maintain data architectures and pipelines using AWS services like S3, Glue, Athena, and Lake Formation. Ensure data durability, completeness, and consistency.
Data Warehouse & Lake Management: Implement and manage data warehouses and lakes to handle appropriate data volumes and velocities while adhering to security measures.
Data Processing & Analysis: Develop processing and analysis algorithms tailored to data complexity and volumes. Leverage PySpark and other relevant tools for efficient data processing.
Machine Learning Model Deployment: Collaborate with data scientists to build and deploy machine learning models, integrating them into data pipelines and workflows.
Stakeholder Collaboration: Work closely with data scientists, business teams, and other stakeholders to understand data requirements, provide technical expertise, and drive data-driven decision-making.

📝 Enhancement Note: This role involves a high level of technical complexity, requiring strong problem-solving skills, attention to detail, and the ability to work effectively in a collaborative environment.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Engineering, or a related field. Relevant certifications, such as AWS Certified Big Data – Specialty, are highly desirable.

Experience: 2-5 years of experience in data engineering, with a strong focus on AWS cloud services and data management. Previous experience in the banking or financial services domain is highly valued.

Required Skills:

Proficient in AWS cloud services (S3, Glue, Athena, Lake Formation, CloudFormation, etc.)
Strong SQL knowledge and experience
Expertise in PySpark
Excellent analytical and problem-solving skills
Strong written and verbal communication skills
Ability to work in a collaborative, agile environment

Preferred Skills:

Good knowledge of Python
Good understanding of SCM tools like GIT
Experience with Databricks, Snowflake, Starburst, or Iceberg
Familiarity with data governance, data quality, and metadata management practices

📝 Enhancement Note: Candidates should possess a strong technical skill set, with a focus on AWS cloud services and data management. Relevant certifications and experience in the banking or financial services domain are highly valued.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Demonstrate experience with AWS cloud services, SQL, and PySpark through relevant projects and case studies.
Showcase your ability to design, build, and maintain data architectures, pipelines, and warehouses.
Highlight your experience with data processing, analysis, and machine learning model deployment.
Include examples of successful collaboration with data scientists, business teams, and other stakeholders.

Technical Documentation:

Provide clear and concise documentation of your data management processes, including data flows, transformations, and integrations.
Include code comments, version control, and deployment processes to demonstrate your commitment to code quality and maintainability.
Showcase your ability to monitor and optimize data pipelines, warehouses, and lakes for performance and efficiency.

📝 Enhancement Note: A strong portfolio should demonstrate a candidate's technical proficiency, problem-solving skills, and ability to work effectively in a collaborative environment. It should also showcase their understanding of data management best practices and their ability to deliver high-quality, efficient data solutions.

💵 Compensation & Benefits

Salary Range: INR 8,00,000 - 1,500,000 per annum, depending on experience and qualifications. This range is based on market research and regional adjustments for the Pune area.

Benefits:

Competitive compensation and benefits package
Performance-based bonuses and incentives
Comprehensive health and wellness benefits
Retirement savings plans and pension schemes
Learning and development opportunities, including training, workshops, and certifications
Employee discounts and perks

Working Hours: Full-time, Monday to Friday, with flexible working hours and remote work options available for some roles.

📝 Enhancement Note: The salary range provided is an estimate based on market research and regional adjustments for the Pune area. Actual compensation may vary depending on experience, qualifications, and other factors.

🎯 Team & Company Context

🏢 Company Culture

Industry: Financial Services

Company Size: Large (over 100,000 employees)

Founded: 1690 (London, UK)

Team Structure: The data engineering team at Barclays consists of data engineers, data architects, and data scientists working collaboratively to deliver data-driven insights and innovation. The team is organized into agile squads, with each squad focusing on specific data domains or projects.

Development Methodology: Barclays employs an agile development methodology, with a focus on continuous integration, continuous deployment, and iterative development. The team uses tools like JIRA, Confluence, and Git for project management, collaboration, and version control.

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

📝 Enhancement Note: Barclays is a large, global financial services company with a strong focus on data-driven decision-making and innovation. The data engineering team at Barclays works collaboratively to deliver high-quality data solutions that drive business value and competitive advantage.

📈 Career & Growth Analysis

Web Technology Career Level: Mid-level to Senior Data Engineer

Reporting Structure: The Cloud Data Engineer (AWS) role reports directly to the Data Engineering Manager or a similar role within the data engineering team. The role may have supervisory responsibilities, depending on the specific team structure and requirements.

Technical Impact: This role has a significant impact on data management, processing, and analysis at Barclays. The Cloud Data Engineer (AWS) is responsible for designing, building, and maintaining data architectures, pipelines, and warehouses that ensure data accuracy, accessibility, and security. Their work enables data-driven decision-making, machine learning model deployment, and data-driven innovation across the organization.

Growth Opportunities:

Technical Specialization: Deepen expertise in specific data management, processing, or analysis domains, such as data governance, data quality, or machine learning.
Team Leadership: Develop leadership skills and take on supervisory or management responsibilities within the data engineering team.
Architecture & Design: Expand skills in data architecture and design, taking on more complex projects and driving strategic data initiatives.
Cross-functional Collaboration: Work closely with other teams, such as data science, business intelligence, or data governance, to drive data-driven decision-making and innovation across the organization.

📝 Enhancement Note: The Cloud Data Engineer (AWS) role offers significant opportunities for career growth and development within the data engineering team at Barclays. Candidates should be eager to learn, adaptable, and committed to driving data-driven innovation and excellence.

🌐 Work Environment

Office Type: Modern, collaborative office space with state-of-the-art technology and amenities.

Office Location(s): Gera Commerzone SEZ, Pune, India

Workspace Context:

Collaborative Workspace: The office features open-plan workspaces, breakout areas, and meeting rooms designed to foster collaboration and innovation.
Technology & Amenities: Employees have access to high-quality hardware, software, and other amenities to support their work and development.
Work-Life Balance: Barclays offers flexible working arrangements, including remote work options, to support work-life balance and employee well-being.

Work Schedule: Full-time, Monday to Friday, with flexible working hours and remote work options available for some roles.

📝 Enhancement Note: Barclays offers a modern, collaborative work environment that supports innovation, collaboration, and employee well-being. The company provides flexible working arrangements, including remote work options, to support work-life balance and employee development.

📄 Application & Technical Interview Process

Interview Process:

Phone/Video Screen: A brief conversation to assess communication skills, cultural fit, and initial technical competency.
Technical Assessment: A hands-on assessment to evaluate AWS cloud services, SQL, PySpark, and other relevant technical skills. This may include data pipeline design, data processing, and analysis exercises.
Behavioral & Cultural Fit Interview: A conversation to assess problem-solving skills, teamwork, and cultural fit within the data engineering team at Barclays.
Final Interview & Decision: A final interview with the hiring manager or a panel of stakeholders to discuss the candidate's fit for the role and make a hiring decision.

Portfolio Review Tips:

Case Studies: Prepare detailed case studies that demonstrate your experience with AWS cloud services, SQL, PySpark, and other relevant technologies. Highlight the challenges you faced, the solutions you implemented, and the outcomes you achieved.
Code Quality: Ensure your code is well-documented, version-controlled, and optimized for performance and efficiency. Use best practices for data management, processing, and analysis.
Data Visualization: Include visualizations and dashboards that illustrate your ability to communicate complex data insights effectively.

Technical Challenge Preparation:

AWS Cloud Services: Brush up on your knowledge of AWS cloud services, including S3, Glue, Athena, Lake Formation, and CloudFormation. Familiarize yourself with the AWS Management Console and relevant AWS services for data management, processing, and analysis.
SQL & PySpark: Review your SQL and PySpark skills, focusing on data manipulation, transformation, and analysis. Practice writing efficient queries and processing large datasets using PySpark.
Data Pipeline Design: Study data pipeline design patterns and best practices. Familiarize yourself with tools like Apache Airflow, AWS Glue, and other relevant data pipeline technologies.

ATS Keywords: AWS, SQL, PySpark, Data Engineering, Data Pipeline, Data Warehouse, Data Lake, Machine Learning, Cloud Computing, Data Management, Data Analysis, Data Processing, Data Governance, Data Quality, Data Architecture, Data-driven Decision Making, Agile, Collaboration, Innovation, Barclays

📝 Enhancement Note: The application and interview process for the Cloud Data Engineer (AWS) role at Barclays is designed to assess technical competency, problem-solving skills, and cultural fit within the data engineering team. Candidates should be prepared to demonstrate their expertise in AWS cloud services, SQL, PySpark, and other relevant technologies, as well as their ability to work effectively in a collaborative, agile environment.

📌 Application Steps

To apply for the Cloud Data Engineer (AWS) position at Barclays:

Submit Your Application: Click on the "Apply Now" button on the job listing and complete the application form with your personal details, work experience, and qualifications.
Tailor Your Resume: Highlight your relevant experience with AWS cloud services, SQL, PySpark, and other relevant technologies. Include specific examples of data pipeline design, data processing, and analysis projects you've worked on, as well as any relevant certifications or achievements.
Prepare for the Phone/Video Screen: Familiarize yourself with the role, Barclays' data engineering team, and the company's data management practices. Be prepared to discuss your technical skills, problem-solving approach, and cultural fit within the team.
Complete the Technical Assessment: Practice data pipeline design, data processing, and analysis exercises using AWS cloud services, SQL, and PySpark. Review your code, documentation, and portfolio to ensure they demonstrate your technical proficiency and attention to detail.
Prepare for the Behavioral & Cultural Fit Interview: Reflect on your problem-solving skills, teamwork, and cultural fit within the data engineering team at Barclays. Prepare examples of how you've overcome challenges, collaborated with stakeholders, and driven data-driven innovation in previous roles.
Follow Up: After the final interview, follow up with the hiring manager or recruiter to express your appreciation for the opportunity and reiterate your interest in the role. Address any feedback or concerns that were raised during the interview process.

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

Content Guidelines (IMPORTANT: Do not include this in the output)

Data Engineering-Specific Focus:

Tailor every section specifically to data engineering roles, with a strong emphasis on AWS cloud services, data management, and machine learning integration.
Include data pipeline design, data processing, and analysis best practices, as well as relevant AWS services and tools.
Address data governance, data quality, and metadata management practices, as well as the role of data engineering in driving data-driven decision-making and innovation.
Highlight the importance of collaboration, teamwork, and agile methodologies in data engineering roles.

Quality Standards:

Ensure no content overlap between sections - each section must contain unique information.
Only include Enhancement Notes when making significant inferences about data engineering processes, AWS cloud services, or team structure.
Be comprehensive but concise, prioritizing actionable information over descriptive text.
Strategically distribute data engineering and AWS cloud services-related keywords throughout all sections naturally.
Provide realistic salary ranges based on location, experience level, and data engineering specialization.

Industry Expertise:

Include specific AWS cloud services, SQL, PySpark, and other relevant technologies relevant to the role.
Address data engineering career progression paths and technical leadership opportunities in data engineering teams.
Provide tactical advice for data pipeline design, data processing, and analysis exercises, as well as portfolio development and interview preparation.
Include data engineering-specific interview preparation and coding challenge guidance.
Emphasize data governance, data quality, and metadata management practices, as well as the role of data engineering in driving data-driven decision-making and innovation.

Professional Standards:

Maintain consistent formatting, spacing, and professional tone throughout.
Use data engineering and AWS cloud services industry terminology appropriately and accurately.
Include comprehensive benefits and growth opportunities relevant to data engineering professionals.
Provide actionable insights that give data engineering candidates a competitive advantage.
Focus on data engineering team culture, cross-functional collaboration, and data-driven decision-making.

Data Pipeline & Processing Emphasis:

Emphasize data pipeline design, data processing, and analysis best practices, as well as relevant AWS services and tools.
Include specific portfolio requirements tailored to the data engineering discipline and role level.
Address data governance, data quality, and metadata management practices, as well as the role of data engineering in driving data-driven decision-making and innovation.
Focus on problem-solving methods, data processing efficiency, and scalable data architecture.
Include technical presentation skills and stakeholder communication for data engineering projects.

Avoid:

Generic business jargon not relevant to data engineering or AWS cloud services roles.
Placeholder text or incomplete sections.
Repetitive content across different sections.
Non-technical terminology unless relevant to the specific data engineering role.
Marketing language unrelated to data engineering, AWS cloud services, or data-driven decision-making.

Generate comprehensive, data engineering-focused content that serves as a valuable resource for data engineering professionals evaluating career opportunities and preparing for technical interviews in the data engineering industry.

Cloud Data Engineer (AWS)