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$133,900–$197,750.
Based on 238 similar postings.
Employer
About Goldman Sachs
Goldman Sachs is a leading global investment banking, securities, and investment management firm providing financial services to corporations, financial institutions, governments, and individuals.
Goldman Sachs currently has
134 open roles
on FindRole.
Listed pay typically runs
$137,000–$250,000
across 55 roles with salary data.
As an Engineering-L2-Dallas-Analyst-Software Engineering on the Data Platform team, you will design, build, and support data pipelines and curated datasets on a modern data platform. You will manage the full lifecycle of data products, including ingestion, transformation, modeling, and optimization to ensure reliability for analytics, reporting, and AI use cases. Your daily work involves developing batch and streaming pipelines, implementing data quality controls, and creating reusable tooling to improve platform functionality. The role requires proficiency in Python or Java, SQL, and distributed processing frameworks like Apache Spark. You will work with data formats such as JSON, Avro, and Parquet within an environment featuring Snowflake, Databricks, and Kafka. You must apply core software engineering principles, including version control and CI/CD, to solve complex technical problems regarding data consistency and scalability.
Build, enhance, and support batch and streaming data pipelines on Lakehouse and AI platforms.
Refactor and modernize existing data flows to improve reliability, performance, and maintainability.
Develop raw, refined, and curated datasets for analytics, reporting, and AI use cases.
Apply data modeling principles to represent business entities and historical changes accurately.
Implement controls to validate the completeness, accuracy, and consistency of data across pipelines.
Build reusable tooling and framework components to improve platform functionality and operational support.
Perform reconciliation to ensure production output integrity and investigate data breaks.
Collaborate with stakeholders to deliver high-quality data products that meet specific business needs.
What we're looking for
Bachelor’s or master’s degree in a relevant discipline, or equivalent practical experience with 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.
Familiarity with software engineering fundamentals, including version control, testing, release discipline, and CI/CD practices.
Experience building or supporting production data pipelines in a collaborative environment.
Experience working with distributed data processing frameworks such as Apache Spark.
Working knowledge of common data formats such as JSON, Avro, and Parquet.
Understanding of temporal data modeling, schema design, and techniques to improve performance at scale.