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Goldman Sachs Software Engineering – Data, Lakehouse & AI Data Platform Engineer (Analyst) Recruitment 2026 | Bengaluru | Placement Officer
Goldman Sachs Analyst Recruitment 2026 – Complete Overview
Executive Summary
Goldman Sachs has announced hiring
for the position of Software Engineering – Data, Lakehouse and AI Data
Platform Engineer (Analyst) at its Bengaluru office. This opportunity is
ideal for software engineers and data engineers with strong expertise in
Python, Java, SQL, Apache Spark, Data Engineering, Data Modeling, and
Distributed Systems.
The role involves building scalable
data pipelines, developing AI-ready data products, ensuring data quality, and
contributing to Goldman Sachs' modern Lakehouse and AI Data Platform ecosystem.
For candidates aspiring to work at
one of the world's most prestigious investment banking and financial technology
firms, this role offers exposure to cutting-edge big data technologies,
cloud-scale architectures, AI platforms, and enterprise-grade engineering
practices.
Job Highlights
|
Particulars |
Details |
|
Company |
Goldman
Sachs |
|
Position |
Software
Engineering – Data, Lakehouse & AI Data Platform Engineer |
|
Level |
Analyst |
|
Job ID |
169320 |
|
Location |
Bengaluru,
Karnataka |
|
Experience
Required |
1+ Years |
|
Qualification |
Bachelor's
or Master's Degree |
|
Department |
Software
Engineering / Data Engineering |
|
Industry |
Investment
Banking & Financial Technology |
|
Job Category |
Analyst |
|
Posted On |
May 14, 2026 |
About Goldman Sachs
Goldman Sachs is a globally
recognized investment banking, securities, and investment management firm
serving corporations, financial institutions, governments, and individuals
worldwide.
Founded in 1869, the organization
operates across major financial markets and is known for its strong engineering
culture, world-class technology teams, and large-scale data-driven innovation.
The firm invests heavily in AI,
data engineering, cloud computing, distributed systems, and modern software
development practices.
Working at Goldman Sachs provides
opportunities to solve complex engineering problems impacting global financial
markets.
Detailed Job Description
The Data, Lakehouse and AI Data
Platform Engineering team is responsible for building and maintaining modern
enterprise-scale data infrastructure.
As a Data Engineer, you will:
·
Design and
develop scalable data pipelines
·
Build AI-ready
datasets
·
Support
large-scale data platforms
·
Ensure data
quality and reliability
·
Optimize
performance of distributed data systems
·
Collaborate
with stakeholders and engineering teams
·
Develop
reusable platform components
This role requires strong software
engineering fundamentals combined with practical data engineering expertise.
Key Responsibilities
Pipeline Engineering
Selected candidates will:
·
Build batch
and streaming data pipelines
·
Support modern
Lakehouse architecture
·
Refactor
legacy data systems
·
Improve
pipeline performance
·
Develop
reusable engineering components
·
Maintain
production-ready systems
Data Modelling & Curation
Responsibilities include:
·
Building raw,
refined, and curated datasets
·
Creating
analytics-ready data products
·
Supporting AI
and machine learning use cases
·
Designing
scalable data models
·
Managing
historical data tracking
Data Quality & Reconciliation
Engineers will:
·
Validate data
completeness
·
Ensure
consistency across datasets
·
Implement
monitoring mechanisms
·
Investigate
production issues
·
Conduct
root-cause analysis
·
Improve
reliability standards
Platform Engineering
Candidates may contribute to:
·
Shared
engineering frameworks
·
Internal
platform tooling
·
Monitoring
systems
·
Testing
frameworks
·
Operational
automation
Eligibility Criteria
Educational Qualification
Candidates should possess:
Bachelor's Degree
·
B.Tech
·
BE
·
B.Sc (Relevant
Technical Disciplines)
Master's Degree
·
M.Tech
·
MS
·
MCA
·
Other Relevant
Technical Degrees
Experience Required
·
Minimum 1 Year
Experience
The role is suitable for:
·
Early-career
Software Engineers
·
Data Engineers
·
Big Data
Engineers
·
Platform
Engineers
Batches Eligible
Based on the experience requirement
and hiring criteria, the likely eligible batches include:
Eligible Batches
·
2022 Batch
·
2023 Batch
·
2024 Batch
·
2025 Batch
Candidates with internship
experience and strong project portfolios may also be considered depending on
their profile.
Essential Skills Required
Programming Skills
Candidates must have strong
hands-on expertise in:
·
Python
·
Java
SQL Expertise
Strong understanding of:
·
Query
Optimization
·
Data Analysis
·
SQL
Troubleshooting
·
Complex Joins
·
Data
Warehousing Concepts
Data Engineering Skills
Knowledge of:
·
Data Pipelines
·
ETL Processes
·
ELT
Architecture
·
Data
Transformation
·
Data Curation
Apache Spark
Experience with:
·
Distributed
Processing
·
Spark SQL
·
Performance
Optimization
·
Large-Scale
Data Processing
Data Modelling
Understanding of:
·
Temporal Data
Modeling
·
Historical
Data Management
·
Schema
Evolution
·
Data
Relationships
·
Data
Governance
Data Formats
Knowledge of:
·
JSON
·
Avro
·
Parquet
Big Data Technologies
Exposure to:
·
Kafka
·
Apache Iceberg
·
Databricks
·
Snowflake
·
Hadoop
Ecosystem
·
Sybase IQ
DevOps & Deployment
Understanding of:
·
Git
·
CI/CD
·
Release
Management
·
Kubernetes
·
Containerized
Deployments
Soft Skills Goldman Sachs Looks For
Successful candidates typically
demonstrate:
·
Problem-Solving
Ability
·
Ownership
Mindset
·
Stakeholder
Communication
·
Technical
Decision-Making
·
Attention to
Detail
·
Team
Collaboration
·
Leadership
Potential
·
Adaptability
Expected Salary (Based on Industry Data)
Goldman Sachs has not officially
disclosed compensation.
Based on compensation trends for
Analyst-level Data Engineering and Software Engineering roles in Bengaluru:
Expected Salary Range
Analyst Level
₹18 LPA – ₹35 LPA
High-Performing Candidates
₹35 LPA+ Total Compensation
Components may include:
·
Fixed Salary
·
Joining Bonus
·
Performance
Bonus
·
Stock
Components (where applicable)
Actual compensation depends on:
·
Experience
·
Technical
Skills
·
Interview
Performance
·
Business
Requirements
Technology Stack You Should Prepare
Core Technologies
·
Python
·
Java
·
SQL
·
Apache Spark
·
Kafka
Data Platforms
·
Snowflake
·
Databricks
·
Hadoop
Cloud & Infrastructure
·
Kubernetes
·
Containerization
·
CI/CD
Pipelines
AI Data Platforms
·
Lakehouse
Architecture
·
AI Data
Engineering
·
Data Product
Development
Why This Role Is Unique
AI + Data Engineering Combination
Few opportunities provide exposure
to both modern AI platforms and enterprise-scale data engineering.
Global Financial Systems
Work on mission-critical platforms
supporting global financial operations.
Cutting-Edge Technology Stack
Exposure to:
·
Lakehouse
Architecture
·
Apache Iceberg
·
Snowflake
·
Databricks
·
Kafka
Strong Career Growth
Potential growth paths include:
·
Senior Data
Engineer
·
Platform
Engineer
·
Engineering
Manager
·
Data Architect
·
AI Platform
Engineer
Pro Tips to Crack Goldman Sachs Data Engineering Interviews
1. Master SQL
Focus on:
·
Window Functions
·
CTEs
·
Query
Optimization
·
Complex
Aggregations
2. Strengthen Apache Spark
Practice:
·
Spark
Transformations
·
Partitioning
·
Performance
Tuning
3. Learn Data Modelling
Prepare concepts like:
·
Star Schema
·
Snowflake
Schema
·
Slowly
Changing Dimensions
·
Temporal
Models
4. Build End-to-End Data Projects
Recommended projects:
·
ETL Pipelines
·
Streaming Data
Systems
·
Kafka-Based
Applications
·
Data Lake
Architectures
5. Revise System Design Basics
Focus on:
·
Scalability
·
Reliability
·
Distributed
Systems
6. Practice Python Coding
Common interview areas:
·
Data
Structures
·
Algorithms
·
Object-Oriented
Programming
·
Problem
Solving
Expected Selection Process
Round 1
Resume Shortlisting
Round 2
Online Coding Assessment
Topics:
·
DSA
·
SQL
·
Python/Java
Round 3
Technical Interview
Topics:
·
Data
Engineering
·
Spark
·
SQL
·
System Design
Round 4
Managerial Round
Round 5
Final Hiring Discussion
Frequently Asked Questions (FAQs)
1. What is the role being offered by Goldman Sachs?
Software Engineering – Data,
Lakehouse and AI Data Platform Engineer (Analyst).
2. What experience is required?
A minimum of 1 year of relevant
experience.
3. Which programming languages are required?
Python or Java.
4. Is SQL mandatory?
Yes. Strong SQL knowledge is one of
the core requirements.
5. What big data technologies should candidates know?
Apache Spark, Kafka, Snowflake,
Databricks, Hadoop, and related technologies.
6. What is the expected salary?
The expected salary range is
approximately ₹18 LPA to ₹35 LPA depending on experience and interview performance.
7. What location is this role based in?
Bengaluru, Karnataka, India.
8. Is this role suitable for software engineers?
Yes. Candidates with software
engineering and data engineering backgrounds are both suitable.
Conclusion
The Goldman Sachs Software
Engineering – Data, Lakehouse and AI Data Platform Engineer (Analyst) role is
one of the most attractive data engineering opportunities available in India in
2026. Combining enterprise-scale data engineering, AI-ready data platforms,
distributed processing technologies, and modern Lakehouse architecture, the
role provides exceptional learning and career growth opportunities.
Candidates with strong Python or Java programming skills, advanced SQL expertise, Apache Spark experience, and a solid understanding of distributed systems will have the strongest chances of success. If you aspire to build cutting-edge data platforms at one of the world's leading financial institutions, this opportunity deserves serious consideration.
🔗 Apply Link: Click Here To Apply for Goldman Sachs
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Disclaimer
The recruitment details shared above are for informational purposes only and
have been obtained from the organization’s official website. We do not
guarantee any job placements. Recruitment will be conducted according to the
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