Engineering-Data Engineering - SRE-Associate-Software Engineering- Bengaluru at Candidate Experience Site

📍 Job Overview

Job Title: Engineering-Data Engineering - SRE-Associate-Software Engineering- Bengaluru
Company: Candidate Experience Site - Lateral
Location: Bengaluru, Karnataka, India
Job Type: On-site
Category: DevOps Engineer, Site Reliability Engineer
Date Posted: 2025-06-21
Experience Level: 2-5 years

🚀 Role Summary

Drive adoption of cloud technology for data processing and warehousing, focusing on reliability, observability, and capacity management.
Engage with data consumers and producers to match reliability and cost requirements, driving SRE strategy for major platforms like Lakehouse and Data Lake.
Collaborate with cross-functional teams to ensure data quality, security, and performance across supply and demand processes.
Utilize your developer background and experience in DevOps and SRE principles to drive strategy with data and deliver impactful solutions.

📝 Enhancement Note: This role requires a strong focus on reliability engineering, data management, and cloud infrastructure. Candidates should be comfortable working in a dynamic, team-focused environment and driving data-driven deliverables.

💻 Primary Responsibilities

Observability & Cost Management: Monitor and optimize the performance, availability, and cost of large-scale data platforms, using tools like Grafana, PromQL, and Open Telemetry.
Platform Strategy: Develop and implement SRE strategies for critical data platforms, ensuring they meet reliability and cost requirements.
Stakeholder Engagement: Collaborate with data consumers and producers to understand their needs and ensure platforms align with their requirements.
Data Quality & Governance: Ensure data quality, security, and performance across supply and demand processes, working with subject matter experts to extract critical business concepts.
Cloud Infrastructure Management: Manage and optimize cloud infrastructure (AWS, Azure, or GCP) to support data processing and warehousing needs.
Code & Automation: Leverage your developer background to automate processes, improve code quality, and drive operational efficiency.

📝 Enhancement Note: This role involves a mix of technical, strategic, and collaborative responsibilities. Candidates should be comfortable working in a multi-dimensional environment and driving impactful solutions.

🎓 Skills & Qualifications

Education: A Bachelor or Master's degree in a computational field (Computer Science, Applied Mathematics, Engineering, or a related quantitative discipline).

Experience: 1-4+ years of relevant work experience in a team-focused environment, with 1-2 years of hands-on developer experience.

Required Skills:

Proficiency in cloud infrastructure (AWS, Azure, or GCP)
Strong understanding of multi-dimensional data, data curation, and data quality
In-depth knowledge of relational and columnar SQL databases, including database design
Expertise in data warehousing concepts (e.g., star schema, entitlement implementations, SQL vs. NoSQL modeling, milestoning, indexing, partitioning)
Excellent communication skills and ability to work with subject matter experts
Strong analytical and problem-solving skills
Independent thinker, willing to engage, challenge, or learn
Strong work ethic, a sense of ownership, and urgency

Preferred Skills:

Understanding of Data Lake / Lakehouse technologies (e.g., Apache Iceberg)
Experience with cloud databases (e.g., Snowflake, Big Query)
Working knowledge of open-source tools (e.g., AWS Lambda, Prometheus)
Experience coding in Java or Python

📝 Enhancement Note: Candidates with experience in data modeling, data governance, and cloud-native technologies will have a competitive advantage in this role.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Demonstrate your ability to manage and optimize large-scale data platforms, highlighting your experience with cloud infrastructure, data warehousing, and data quality.
Showcase your problem-solving skills and ability to drive data-driven deliverables through case studies or projects.
Highlight your experience with data modeling, data governance, and cloud-native technologies, if applicable.

Technical Documentation:

Document your approach to data quality, security, and performance, including any relevant metrics, testing methodologies, and optimization techniques.
Include any relevant code samples or snippets demonstrating your proficiency in Python, Java, or other relevant languages.

📝 Enhancement Note: Tailor your portfolio to emphasize your experience with data warehousing, cloud infrastructure, and data quality, highlighting any relevant projects or case studies.

💵 Compensation & Benefits

Salary Range: INR 1,200,000 - INR 2,000,000 per annum (estimated, based on market research and role requirements)

Benefits:

Competitive compensation package
Comprehensive health and wellness benefits
Retirement savings plans
Employee stock purchase plan
Tuition assistance and professional development opportunities
Global work environment with diverse teams and cultures

Working Hours: 40 hours per week, with flexibility for project deadlines and maintenance windows.

📝 Enhancement Note: The salary range provided is an estimate based on market research and role requirements. Actual compensation may vary based on factors such as experience, skills, and company performance.

🎯 Team & Company Context

🏢 Company Culture

Industry: Financial Services

Company Size: Large (global organization with multiple teams and locations)

Founded: 1869 (with a rich history and established culture)

Team Structure:

Collaborative, cross-functional teams working on data processing, warehousing, and management projects.
Close collaboration with business units, ensuring data platforms meet their needs and requirements.
A global organization with diverse teams and cultures, offering opportunities for growth and development.

Development Methodology:

Agile and iterative development processes, focusing on continuous improvement and innovation.
Strong emphasis on data-driven decision-making and impactful solutions.
Collaborative environment with regular code reviews, testing, and quality assurance practices.

Company Website: Global Banking Markets

📝 Enhancement Note: Goldman Sachs is a well-established financial services firm with a strong focus on data management, innovation, and client service. This role offers the opportunity to work in a dynamic, global environment with diverse teams and cultures.

📈 Career & Growth Analysis

Web Technology Career Level: Mid-level Site Reliability Engineer (SRE) or Associate Software Engineer, focusing on data engineering and cloud infrastructure management.

Reporting Structure: This role reports directly to the Data Engineering team lead, with close collaboration with business units and other technical teams.

Technical Impact: Responsible for ensuring the reliability, performance, and cost-effectiveness of large-scale data platforms, driving impactful solutions and enabling business deliverables.

Growth Opportunities:

Develop expertise in data warehousing, cloud infrastructure, and data quality, driving impactful solutions and enabling business deliverables.
Gain experience working on large-scale, complex data projects, honing your problem-solving and strategic thinking skills.
Collaborate with diverse teams and stakeholders, expanding your professional network and learning from industry experts.

📝 Enhancement Note: This role offers opportunities for growth and development in data engineering, cloud infrastructure management, and strategic thinking. Candidates can expect to work on large-scale, complex data projects and collaborate with diverse teams and stakeholders.

🌐 Work Environment

Office Type: Modern, collaborative workspaces with state-of-the-art technology and amenities, fostering a productive and innovative environment.

Office Location(s): Bengaluru, India (with opportunities for global collaboration and travel)

Workspace Context:

Collaborative workspaces with multiple monitors, testing devices, and development tools available.
Access to global teams and resources, enabling cross-functional collaboration and knowledge sharing.
Flexible work arrangements, with opportunities for remote work and hybrid schedules.

Work Schedule: 40 hours per week, with flexibility for project deadlines, maintenance windows, and global collaboration.

📝 Enhancement Note: Goldman Sachs offers a modern, collaborative work environment with state-of-the-art technology and amenities, fostering a productive and innovative environment. Candidates can expect to work in a dynamic, global setting with diverse teams and cultures.

📄 Application & Technical Interview Process

Interview Process:

Online Assessment: Complete an online assessment to evaluate your problem-solving skills, technical proficiency, and cultural fit.
Technical Phone Screen: Participate in a technical phone screen to discuss your experience, skills, and approach to data engineering and cloud infrastructure management.
On-site Interview: Attend an on-site interview at the Bengaluru office, where you will meet with team members, discuss your portfolio, and participate in technical challenges and case studies.
Final Decision: Receive a final decision and, if successful, an offer of employment.

Portfolio Review Tips:

Highlight your experience with data warehousing, cloud infrastructure, and data quality, using case studies or projects to demonstrate your skills and impact.
Include any relevant code samples or snippets, demonstrating your proficiency in Python, Java, or other relevant languages.
Tailor your portfolio to showcase your problem-solving skills and ability to drive data-driven deliverables.

Technical Challenge Preparation:

Brush up on your knowledge of data warehousing concepts, cloud infrastructure, and data quality.
Practice problem-solving and strategic thinking exercises, focusing on data-driven decision-making and impactful solutions.
Familiarize yourself with the Goldman Sachs culture and values, preparing thoughtful questions and responses that demonstrate your cultural fit.

ATS Keywords: (Organized by category)

Programming Languages: Python, Java, SQL, JavaScript
Cloud Infrastructure: AWS, Azure, GCP, Snowflake, BigQuery
Data Warehousing: Star schema, entitlement implementations, milestoning, indexing, partitioning
Data Quality: Traceability, security, performance latency, correctness, data curation
DevOps & SRE: Reliability, observability, capacity management, DevOps, SDLC, cloud-native technologies
Soft Skills: Communication, collaboration, problem-solving, strategic thinking, data-driven decision-making

📝 Enhancement Note: The interview process for this role is designed to evaluate your technical proficiency, problem-solving skills, and cultural fit. Be prepared to discuss your experience with data warehousing, cloud infrastructure, and data quality, using case studies or projects to demonstrate your skills and impact.

🛠 Technology Stack & Web Infrastructure

Frontend Technologies: (Not applicable, as this role focuses on data engineering and cloud infrastructure management)

Backend & Server Technologies:

Cloud infrastructure: AWS, Azure, GCP
Data warehousing: Snowflake, BigQuery
Relational and columnar SQL databases: PostgreSQL, MySQL, Oracle
Data modeling and ETL tools: Talend, Informatica, Pentaho
Programming languages: Python, Java, SQL, JavaScript

Development & DevOps Tools:

Version control: Git, GitLab
CI/CD pipelines: Jenkins, CircleCI, GitLab CI/CD
Monitoring and logging: Prometheus, Grafana, ELK Stack
Infrastructure as Code (IaC): Terraform, CloudFormation, Azure Resource Manager
Containerization: Docker, Kubernetes

📝 Enhancement Note: This role requires proficiency in cloud infrastructure, data warehousing, and data quality, with a strong focus on data engineering and cloud infrastructure management. Candidates should be comfortable working with relevant technologies and tools.

👥 Team Culture & Values

Web Development Values:

Data-Driven Decision Making: Utilize data to inform strategy, optimize processes, and drive impactful solutions.
Collaboration & Communication: Work closely with cross-functional teams, stakeholders, and business units to ensure data platforms meet their needs and requirements.
Innovation & Continuous Learning: Stay up-to-date with emerging technologies, trends, and best practices in data engineering and cloud infrastructure management.
Reliability & Resilience: Ensure data platforms are reliable, resilient, and capable of handling large-scale, complex data workloads.

Collaboration Style:

Cross-Functional Teams: Work closely with business units, data producers, and data consumers to ensure data platforms meet their needs and requirements.
Code Reviews & Pair Programming: Collaborate with team members to review code, share knowledge, and improve overall code quality.
Knowledge Sharing: Contribute to a culture of learning and growth by sharing your expertise and experiences with team members and stakeholders.

📝 Enhancement Note: Goldman Sachs values data-driven decision-making, collaboration, innovation, and continuous learning. Candidates should be comfortable working in a dynamic, cross-functional environment and driving impactful solutions through data engineering and cloud infrastructure management.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Data Quality & Governance: Ensure data quality, security, and performance across supply and demand processes, working with subject matter experts to extract critical business concepts.
Cloud Infrastructure Management: Manage and optimize cloud infrastructure (AWS, Azure, or GCP) to support data processing and warehousing needs, driving cost-efficiency and scalability.
Data Modeling & Warehousing: Design and implement data models, ensuring they meet the needs of data consumers and producers while optimizing performance and cost-effectiveness.
Emerging Technologies: Stay up-to-date with emerging technologies, trends, and best practices in data engineering and cloud infrastructure management, driving innovation and impactful solutions.

Learning & Development Opportunities:

Data Engineering: Develop expertise in data warehousing, cloud infrastructure, and data quality, driving impactful solutions and enabling business deliverables.
Cloud Infrastructure Management: Gain experience working on large-scale, complex data projects, honing your problem-solving and strategic thinking skills.
Leadership & Mentoring: Collaborate with diverse teams and stakeholders, expanding your professional network and learning from industry experts.

📝 Enhancement Note: This role offers opportunities for growth and development in data engineering, cloud infrastructure management, and strategic thinking. Candidates can expect to work on large-scale, complex data projects and collaborate with diverse teams and stakeholders.

💡 Interview Preparation

Technical Questions:

Data Warehousing: Describe your experience with data warehousing concepts, such as star schema, entitlement implementations, milestoning, indexing, and partitioning. Provide examples of how you've optimized data models for performance and cost-effectiveness.
Cloud Infrastructure: Discuss your experience with cloud infrastructure (AWS, Azure, or GCP), highlighting your approach to cost management, scalability, and resilience. Provide examples of how you've optimized cloud infrastructure for data processing and warehousing needs.
Data Quality: Explain your approach to data quality, security, and performance, including any relevant metrics, testing methodologies, and optimization techniques. Provide examples of how you've ensured data quality across supply and demand processes.

Company & Culture Questions:

Data-Driven Decision Making: Describe your experience with data-driven decision-making, providing examples of how you've used data to inform strategy, optimize processes, and drive impactful solutions.
Collaboration & Communication: Discuss your experience working with cross-functional teams, stakeholders, and business units. Provide examples of how you've ensured data platforms meet their needs and requirements.
Innovation & Continuous Learning: Share your approach to staying up-to-date with emerging technologies, trends, and best practices in data engineering and cloud infrastructure management. Provide examples of how you've driven innovation and impactful solutions in previous roles.

Portfolio Presentation Strategy:

Data Warehousing: Highlight your experience with data warehousing concepts, using case studies or projects to demonstrate your skills and impact. Include any relevant code samples or snippets, demonstrating your proficiency in Python, Java, or other relevant languages.
Cloud Infrastructure: Showcase your experience with cloud infrastructure (AWS, Azure, or GCP), highlighting your approach to cost management, scalability, and resilience. Include any relevant diagrams, visualizations, or other supporting materials.
Data Quality: Demonstrate your approach to data quality, security, and performance, using case studies or projects to illustrate your skills and impact. Include any relevant metrics, testing methodologies, and optimization techniques.

📝 Enhancement Note: Prepare thoroughly for the technical interview, focusing on your experience with data warehousing, cloud infrastructure, and data quality. Tailor your portfolio to showcase your skills, impact, and cultural fit, using case studies, projects, and supporting materials to demonstrate your expertise.

📌 Application Steps

To apply for this data engineering and cloud infrastructure management position at Goldman Sachs:

Submit Your Application: Visit the Candidate Experience Site - Lateral and submit your application through the application link provided.
Tailor Your Portfolio: Highlight your experience with data warehousing, cloud infrastructure, and data quality, using case studies or projects to demonstrate your skills and impact. Include any relevant code samples or snippets, demonstrating your proficiency in Python, Java, or other relevant languages.
Optimize Your Resume: Emphasize your experience with data engineering, cloud infrastructure management, and data quality, using relevant keywords and achievements to demonstrate your qualifications.
Prepare for Technical Challenges: Brush up on your knowledge of data warehousing concepts, cloud infrastructure, and data quality. Practice problem-solving and strategic thinking exercises, focusing on data-driven decision-making and impactful solutions.
Research the Company: Familiarize yourself with Goldman Sachs' culture, values, and business, preparing thoughtful questions and responses that demonstrate your cultural fit.

⚠️ 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.

Engineering-Data Engineering - SRE-Associate-Software Engineering- Bengaluru