Description

Job Description

Summary:

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 Datastore to AWS hosted Lakehouse. This is a high visibility and crucial project for our client.

Responsibilities:

1. Pipeline Migration & Modeling

a. Logic & Scheduling: Refactoring and migrating extraction logic and job scheduling from legacy frameworks to the new Lakehouse environment.
b. Data Transfer: Execute the physical migration of underlying datasets while ensuring data integrity.
c. 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.

2. Code Conversion

a. Convert legacy code and optimize legacy SQL and Spark-based consumption patterns (raw and modeled) for compatibility with Snowflake and Iceberg.
b. Usage analysis: Understand usage patterns to deliver the required data products.
c. 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.

3. 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 internal data management platforms and must have an aptitude for learning new workflows and language constructs as necessary.

Technical Skills:

1. Basic Qualifications

a. Education: Bachelor’s or Master’s in Computer Science, Applied Mathematics, Engineering, or a related quantitative field.
b. Experience: Minimum of 3–10 years of professional hands-on experience working on data migration in a collaborative, team-based environment. Ability to troubleshoot (SQL) and basic scripting experience.
c. Languages: Professional proficiency in Python or Java.
d. Methodology: Deep familiarity with Software Development Life Cycle (SDLC) and CI/CD best practices & K8s deployment experience.

2. Core Data Concepts

Candidates must demonstrate a sophisticated understanding of the following concepts to ensure data correctness during reconciliation:

a. Temporal Data Modeling: Managing state changes over time (e.g., SCD Type 2).
b. Schema Management: Expertise in Schema Evolution (Ref: Iceberg Apache) and enforcement strategies.
c. Performance Optimization: Advanced knowledge of data partitioning and clustering.
d. Architectural Theory: Balancing normalization vs. denormalization and the strategic use of Natural vs. Surrogate Keys.

3. 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
• Data Formats: JSON, Avro, Parquet
• Platforms: Hadoop (HDFS/Hive), Snowflake, Apache Iceberg, Sybase IQ

Core Competencies:

• Demonstrates strong integrity and consistently models good conduct and ethical decision-making.
• Acts as a trusted team player who collaborates effectively across multiple teams and functions.
• Communicates with clarity and confidence – concise written updates, structured verbal briefings, and proactive stakeholder management.
• Works effectively with global teams across time zones and cultures; builds alignment and resolves issues constructively.
• Delivers high-quality work at a strong sense of ownership; drives work to closure and meets commitments.
• Brings high energy and urgency to achieve targets while maintaining quality and professionalism.
• Shows intellectual curiosity, asks thoughtful questions, surfaces risks early, and seeks feedback to continuously improve

Key Skills

Education

Any Graduate