Data Engineer

Apexon; Dallas (TX); On-site; USD 120,000 - 180,000; Full time

Job summary

Goldman Sachs is seeking a data engineer to join the datastore-migration Factory team in Dallas. You will drive end-to-end migration from on-prem DataLake to AWS LakeHouse, refactor extraction logic, and migrate scheduling within the new environment.

You will handle data transfers, validate data with reconciliation frameworks, collaborate with data owners, and ensure artifacts meet business requirements while learning new workflows and languages as needed.

Qualifications

• Bachelor’s or Master’s in CS, Applied Math, Engineering, or related field.
• 3–5 years of hands-on coding in a team environment; able to troubleshoot SQL; basic scripting.
• Proficiency in Python or Java.
• Strong SDLC and CI/CD knowledge; Kubernetes deployment experience.

Responsibilities

• Refactor and migrate extraction logic and job scheduling from legacy frameworks to Lakehouse.
• Execute physical migration of datasets while maintaining data integrity.
• Engage with internal clients to hand off and sign off migrated assets.
• Translate legacy SQL/Spark consumption patterns for Snowflake and Iceberg.
• Validate migrated data against production flows using reconciliation frameworks.

Skills

Python

Java

SQL debugging

CI/CD

Kubernetes

SDLC

Data engineering

Education

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

Tools

Snowflake

Apache Iceberg

Sybase IQ

Hadoop/HDFS/Hive

Job description

Engineer will be part of the datastore-migration Factory team that will be responsible to perform for the end-to end datastore migration from on-prem DataLake to AWS hosted LakeHouse. This is a high visibility and crucial project for Goldman Sachs.

• Logic & Scheduling: Refactoring and migrating extraction logic and job scheduling from legacy frameworks to the new Lakehouse environment.
• Data Transfer: Executing the physical migration of underlying datasets while ensuring data integrity.
• Stakeholder Engagement: Acting as a technical liaison to internal clients, facilitating "hand-off and sign-off" conversations with data owners to ensure migrated assets meet business requirements.

Consumption Pattern Migration

• Code Conversion: Translating and optimizing legacy SQL and Spark-based consumption patterns (raw and modeled) for compatibility with Snowflake and Iceberg.
• Usage analysis: Understand usage patterns to deliver the required data products.
• Stakeholder Engagement: Acting as a technical liaison to internal clients, facilitating "hand-off and sign-off" conversations with data owners to ensure migrated assets meet business requirements.
• A rigorous approach to data validation is required. Candidates must work with reconciliation frameworks to build confidence that migrated data is functionally equivalent to that already used within production flows.
• Engineer will also need to work with internal data management platforms team and must have an aptitude for learning new workflows and language constructs as necessary.

Technical Skills:

• Education: Bachelor’s or Masters in Computer Science, Applied Mathematics, Engineering, or a related quantitative field.
• Experience: Minimum of 3-5 years of professional "hands-on-keyboard" coding experience in a collaborative, team-based environment. Ability to trouble shoot (SQL) and basic scripting experience.
• Languages: Professional proficiency in Python or Java.
• Methodology: Deep familiarity with the full Software Development Life Cycle (SDLC) and CI/CD best practices & K8s deployment experience.

Core Data Engineering Competencies:

• Candidates must demonstrate a sophisticated understanding of the following modeling concepts to ensure data correctness during reconciliation: Temporal Data Modeling: Managing state changes over time (e.g., SCD Type 2).
• Schema Management: Expertise in Schema Evolution (Ref: Iceberg Apache) and enforcement strategies.
• Performance Optimization: Advanced knowledge of data partitioning and clustering.
• Architectural Theory: Balancing Normalization vs. Denormalization and the strategic use of Natural vs. Surrogate Keys.

Technical Stack Requirements:

• While candidates are not expected to be experts in every tool, the collective team must cover the following technologies:
• Extraction & Logic
• Data Formats
• Platforms
• Hadoop (HDFS/Hive), Snowflake, Apache Iceberg, Sybase IQ