High visibility and crucial project for Goldman Sachs
Minimum of 3-5 years of professional coding experience
Role overview
Responsibilities
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.
Pipeline Migration: Logic & Scheduling: Refactoring and migrating extraction logic and job scheduling from legacy frameworks to the new Lakehouse environment.
Pipeline Migration: Data Transfer: Executing the physical migration of underlying datasets while ensuring data integrity.
Pipeline Migration: Stakeholder Engagement: Acting as a technical liaison to internal clients, facilitating "handoff 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.
Consumption Pattern Migration: Usage analysis: Understand usage patterns to deliver the required data products.
Consumption Pattern Migration: Stakeholder Engagement: Acting as a technical liaison to internal clients, facilitating "handoff and sign-off" conversations with data owners to ensure migrated assets meet business requirements.
Data Reconciliation & Quality: 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 our other internal data management platform, and must have an aptitude for learning new workflows and language constructs as necessary.
Requirements
Education: Bachelor’s or Master’s degree 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).
Core Data Engineering Competencies: Schema Management: Expertise in Schema Evolution (Ref: Iceberg Apache) and enforcement strategies.
Core Data Engineering Competencies: Performance Optimization: Advanced knowledge of data partitioning and clustering.
Core Data Engineering Competencies: 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: Kafka, ANSI SQL, FTP, Apache Spark.
Technical Stack Requirements: Data Formats: JSON, Avro, Parquet.
Core Competencies: Demonstrates strong integrity and consistently models good conduct and ethical decision-making.
Core Competencies: Acts as a trusted team player who collaborates effectively across multiple teams and functions.
Core Competencies: Communicates with clarity and confidence - concise written updates, structured verbal briefings, and proactive stakeholder management.
Core Competencies: Works effectively with global teams across time zones and cultures; builds alignment and resolves issues constructively.
Core Competencies: Delivery-focused with a strong sense of ownership; drives work to closure and meets commitments.
Core Competencies: Brings high energy and urgency to achieve targets while maintaining quality and professionalism.
Core Competencies: Shows intellectual curiosity; asks thoughtful questions, surfaces risks early, and seeks feedback to continuously improve.
Additional details
This is a high visibility and crucial project for Goldman Sachs.
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