Engineering-L2-Bengaluru-Analyst-Software Engineering

Goldman Sachs; Bengaluru; On-site; INR 900,000 - 1,800,000; Full time

Job summary

Goldman Sachs in Bengaluru seeks a data engineer to design and maintain robust data pipelines, models, and quality controls. You will work on batch and streaming data, building reliable data products for analytics and AI use cases.

Strong Python/Java, SQL and Spark skills are required, with a focus on production-grade delivery. The role emphasizes collaboration with platform teams and data consumers, with opportunities for technical leadership and mentorship within a dynamic, fast-paced

Qualifications

• Bachelor's or Master's degree in a relevant discipline, or equivalent practical experience.
• 1–3 years of experience in data engineering or related field.
• Strong hands-on programming in Python or Java.
• Good working knowledge of SQL, optimization and data analysis.
• Experience with Apache Spark and distributed data processing.
• Knowledge of JSON, Avro and Parquet data formats.
• Familiarity with CI/CD, version control and testing practices.
• Understanding of temporal data modelling and data quality concepts.
• Ability to learn new tools and internal platforms quickly.

Responsibilities

• Build, refactor and support batch and streaming data pipelines.
• Develop raw to curated datasets for analytics and AI use cases.
• Implement data quality controls and reconciliation methods.
• Collaborate with engineers and data consumers to deliver outcomes.
• Provide technical leadership for delivery and mentorship where needed.

Skills

Python

Java

SQL

Apache Spark

Data engineering

CI/CD

Data modeling

Data quality

Education

Bachelor's or Master's in a relevant discipline

Tools

Git

Parquet

Avro

JSON

Job description

Job Description

What We Do

At Goldman Sachs, our Engineers don't just make things – we make things possible. Change the world by connecting people and capital with ideas. Solve the most challenging and pressing engineering problems for our clients. Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action. Create new businesses, transform finance, and explore a world of opportunity at the speed of markets.

Engineering, which is comprised of our Technology Division and global strategists groups, is at the critical center of our business, and our dynamic environment requires innovative strategic thinking and immediate, real solutions. Want to push the limit of digital possibilities? Start here.

Key Responsibilities

Pipeline Engineering

• Build, enhance and support batch and streaming data pipelines on the Lakehouse and AI data platform.
• Refactor or modernize existing data flows where needed to improve reliability, performance and maintainability.
• Where needed, build reusable tooling to improve delivery, consistency and operational support.
• Ensure data pipelines are production-ready, well tested and operationally supportable.

Data Modelling and Curation

• Develop raw, refined and curated datasets that support analytics, reporting and AI use cases.
• Apply sound data modelling principles to represent business entities, relationships and historical change accurately.
• Work with consumers to shape data products that are usable, well documented and aligned to business needs.

Data Quality and Reconciliation

• Implement controls to validate completeness, accuracy and consistency of data across pipelines and datasets.
• Use reconciliation approaches to build confidence in production outputs and investigate breaks where they arise.
• Contribute to clear standards for testing, monitoring and issue resolution.
• Contribute to practical improvements in testing, monitoring or reconciliation tooling where these strengthen platform reliability and day-to-day delivery.

Delivery and Partnership

• Work closely with engineers, platform teams and data consumers to deliver agreed outcomes to time and quality expectations.
• Communicate clearly on progress, risks, dependencies and design choices, including where delivery would benefit from improvements to shared platform tooling.
• For more senior candidates, take a broader role in technical leadership, task breakdown and support for junior engineers.

Required

Skills and Experience

• 1-3 years of experience
• Bachelor's or master's degree in a relevant discipline, or equivalent practical experience, with evidence of strong quantitative skills or data engineering expertise.
• Strong hands-on programming experience in Python or Java.
• Good working knowledge of SQL, including troubleshooting, optimization and data analysis.
• Ability to learn new tools, internal platforms and delivery workflows quickly.
• Familiarity with software engineering fundamentals, including version control, testing, release discipline and CI/CD practices.
• Understanding of temporal data modelling, including the handling of historical state and change over time.
• Knowledge of schema design, schema evolution and data compatibility considerations.
• Understanding of partitioning, clustering and other techniques used to improve data performance at scale.
• Ability to make sensible design choices across normalized and denormalized models, and between natural and surrogate keys.
• Practical approach to data quality, reconciliation and root-cause analysis.
• Experience building or supporting production data pipelines in a collaborative engineering environment.
• Experience working with distributed data processing frameworks such as Apache Spark.
• Working knowledge of common data formats such as JSON, Avro and Parquet.
• Stronger ownership of technical design across multiple datasets or pipeline domains.
• Experience guiding implementation standards, code quality and engineering practices within a team.
• Ability to lead delivery for a workstream, manage dependencies and support less experienced engineers.

What We Are Looking For

We are looking for engineers who can deliver well-structured, reliable solutions in production and who take ownership of the quality of what they build. The role suits candidates who are technically strong, pragmatic and comfortable working in a fast-paced environment where data platforms support important business outcomes.

Stronger Candidates Will Typically Demonstrate

• sound judgement in technical trade-offs
• attention to detail in data correctness and testing
• a clear and structured approach to problem solving
• willingness to work closely with stakeholders and partner teams
• an interest in developing long-term expertise within the firm

About Goldman Sachs

At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.

We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers.

We're committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html

© The Goldman Sachs Group, Inc., 2023. All rights reserved.

Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.