Data Migration Engineer

Data Migration Engineer

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.

Job Location

Dallas, TX (Day One Onsite - 5 days in a week)

Key Responsibilities

• Pipeline Migration: 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 handoff and signoff 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.
• 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.
• Data Engineer Collaboration: 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.

Required Skills

• Experience: Minimum of 3-5 years of professional hands-on keyboard coding experience in a collaborative, team-based environment. Ability to troubleshoot 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.
• Technologies: 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.

Life At Capgemini

Capgemini supports all aspects of your well-being throughout the changing stages of your life and career. For eligible employees, we offer:

• Flexible work
• Healthcare including dental, vision, mental health, and well-being programs
• Financial well-being programs such as 401(k) and Employee Share Ownership Plan
• Paid time off and paid holidays
• Paid parental leave
• Family building benefits like adoption assistance, surrogacy, and cryopreservation
• Social well-being benefits like subsidized back-up child/elder care and tutoring
• Mentoring, coaching and learning programs
• Employee Resource Groups
• Disaster Relief

Key skills/competency

• Data Migration
• Data Engineering
• Python
• Java
• SQL
• Apache Spark
• Snowflake
• Iceberg
• SDLC
• CI/CD