Data Engineer

Delta System & Software, Inc.; Dallas (TX); On-site; USD 110,000 - 150,000; Full time

Job summary

Goldman Sachs seeks an experienced Data Engineer to join the Datastore Migration Factory, migrating on-prem data lakes to an AWS Lakehouse. You’ll drive data pipelines, ensure data integrity, and optimize workloads for Snowflake and Iceberg, collaborating with global teams to deliver a production-ready migration.

You’ll leverage Python/Java, ANSI SQL, Spark, and modern data architectures, with strong emphasis on quality, governance, and stakeholder communication throughout the project lifecycle.

Qualifications

• Bachelor’s or Master’s degree in CS, Engineering, Applied Mathematics, or related field.
• 3–5 years hands-on software development or data engineering experience.
• Strong SQL troubleshooting and scripting skills.
• Experience working in collaborative, team-based engineering environments.
• Excellent communication, stakeholder management, and problem-solving skills.

Responsibilities

• Migrate on-prem data pipelines to AWS Lakehouse environment.
• Migrate extraction logic and job scheduling from legacy to AWS Lakehouse.
• Ensure data integrity and consistency during migrations.
• Convert and optimize legacy SQL and Spark workloads for Snowflake and Iceberg.
• Analyze data consumption patterns and deliver business-ready data products.
• Perform data reconciliation, validation, and quality checks.
• Collaborate with data owners and stakeholders for migration handoffs and sign-offs.
• Work with cross-functional teams to troubleshoot issues and ensure successful delivery.

Skills

Python

ANSI SQL

Apache Spark

AWS

Snowflake

Kafka

Hadoop

ETL/ELT

Data Modeling

CI/CD

Kubernetes

Education

Bachelor’s or Master’s degree in CS/Engineering/Math

Tools

Kubernetes

Spark

Snowflake

Job description

We are looking for an experienced Data Engineer to join the Datastore Migration Factory team for a high-visibility project at Goldman Sachs, focused on migrating on-premises Data Lakes to an AWS-hosted Lakehouse environment.

The ideal candidate will have strong coding skills, SQL expertise, data migration experience, and a solid understanding of modern data engineering architectures.

Migrate data pipelines, extraction logic, and job scheduling from legacy frameworks to the AWS Lakehouse environment.

Execute dataset migrations while ensuring data integrity and consistency.

Convert and optimize legacy SQL and Spark-based workloads for Snowflake and Apache Iceberg.

Analyze data consumption patterns and support the delivery of business-ready data products.

Perform data reconciliation, validation, and quality checks to ensure functional equivalence.

Collaborate with data owners and stakeholders to coordinate migration handoffs and sign-offs.

Work with cross-functional teams to troubleshoot issues and ensure successful project delivery.

Required Technical Skills

• Programming: Python or Java
• SQL & Data Processing: ANSI SQL, Apache Spark, Kafka
• Data Platforms: AWS, Snowflake, Apache Iceberg, Hadoop, HDFS/Hive, Sybase IQ
• Data Engineering: ETL/ELT, pipeline migration, data reconciliation, and quality validation
• Data Modeling: SCD Type 2, schema evolution, partitioning, clustering, normalization, denormalization, and key management
• Engineering Practices: SDLC, CI/CD, Kubernetes (K8s), scripting, and troubleshooting

Bachelor’s or Master’s degree in Computer Science, Engineering, Applied Mathematics, or a related quantitative field.

3–5 years of hands-on software development or data engineering experience.

Strong SQL troubleshooting and scripting skills.

Experience working in collaborative, team-based engineering environments.

Excellent communication, stakeholder management, and problem-solving skills.

A delivery-focused engineer with strong ownership, attention to data quality, a collaborative mindset, and the ability to work effectively with global teams.

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