Goldman Sachs is a leading global investment banking, securities and investment management firm that provides a wide range of services worldwide to a substantial and diversified client base that includes corporations, financial institutions, governments and high net-worth individuals. Founded in 1869, it is one of the oldest and largest investment banking firms.
Core Engineering provides global thought leadership, solution development, delivery and support for a broad suite of technology products and services at Goldman Sachs. It leverages global expertise to develop leading-edge technology solutions that meet business demands and differentiate the firm's technology offerings in the marketplace. Core Engineering enables the operation of the firm by managing an extensive compute platform and global communication networks, while addressing technology, compliance and reputational risk and resilience.
About The Role
Goldman Sachs employs thousands of engineers across many divisions. We write software that powers every aspect of our business. It's no surprise that we collect and analyze vast amounts of data relating to the efficiency of our development processes, the value that we drive through software, and the effectiveness of how we leverage our internal tooling and services to empower development. Our data allows our internal businesses to optimize, focus their attention and manage costs. It allows us to assess the impact and performance of developer productivity initiatives, cost saving initiatives, and manage our huge software inventory.
What We Need
We are seeking a highly skilled Data Software Engineer to design, implement, and maintain robust data systems and pipelines that empower our organization to leverage data effectively. The ideal candidate will come from a software engineering background, with commercial development experience in one or more object-oriented languages (Python, Go, Java, C#). Knowledge of data modeling, pipeline construction, data normalization and sanitization, and data governance would be highly advantageous. They will collaborate with cross-functional teams to ensure the availability, quality, and security of data to support business objectives.
Key Responsibilities
Data Pipeline Development Design, build, and maintain scalable and efficient data pipelines for processing and transforming large datasets.Ensure pipelines are optimized for reliability and performance.Data Modeling and Architecture Develop and maintain logical and physical data models tailored to business needs.Optimize data storage solutions for scalability and performance.Data Quality and Sanitization Implement processes for data normalization, deduplication, and cleaning to ensure high-quality datasets.Identify and resolve data inconsistencies, errors, and anomalies.Cross-functional Collaboration Work with data scientists, analysts, and business stakeholders to understand data requirements and deliver solutions.Support AI/ML initiatives by ensuring timely and accurate data delivery.Monitoring and Optimization Develop monitoring solutions to ensure the health and reliability of data pipelines and systems.Continuously optimize performance, storage, and costs of data infrastructure.Qualifications
Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.Experience: 4+ years of experience in software engineering, data engineering or a related role.Experience with building data platform or data governance related projects.Technical Skills: Proficiency in programming languages (prefer Java).Strong knowledge of SQL and relational databases (e.g., PostgreSQL, Sybase, SQL Server).Soft Skills: Strong analytical and problem-solving abilities.Effective communication and collaboration skills to work with diverse teams.Attention to detail and a proactive approach to ensuring data integrity.Preferred Qualifications
Proven experience with data modeling, ETL/ELT pipelines, and data architecture design.Knowledge of data lake architectures and unstructured data processing.Experience with big data technologies (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, GCP, Azure).Familiarity with data integration tools (e.g., Apache Airflow, Talend, Informatica) and streaming technologies (e.g., Kafka, Flink).Hands-on experience with data warehouse technologies (e.g., Snowflake, Redshift, BigQuery).