Data Engineer (On-Site Dallas)

NTT DATA • Dallas, TX

1d • Onsite

About The Position

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.
We are currently seeking a Data Engineer (On-Site Dallas) to join our team in Dallas, Texas (US-TX), United States (US). This role will be on-site 5 days/week.
Data Engineer – Job Description (Dallas, TX)
Day to Day job Duties:
Engineer will be part of the datastore-migration Factory team responsible for end-to-end datastore migration from on-prem DataLake to AWS hosted LakeHouse. This is a high visibility and crucial project.
Refactoring and migrating extraction logic and job scheduling from legacy frameworks to the new Lakehouse environment.
Executing the physical migration of underlying datasets while ensuring data integrity.
Acting as a technical liaison to internal clients, facilitating handoff and sign-off conversations with data owners.
Translating and optimizing legacy SQL and Spark-based consumption patterns for compatibility with Snowflake and Iceberg.
Understanding usage patterns to deliver the required data products.
Working on data reconciliation frameworks to ensure migrated data is functionally equivalent to production data.
Working with internal data management platforms and learning new workflows and language constructs as necessary.
Core Data Engineering Competencies:
Temporal Data Modeling: Managing state changes over time (e.g., SCD Type 2).
Schema Management: Expertise in Schema Evolution (Apache Iceberg) and enforcement strategies.
Performance Optimization: Knowledge of data partitioning and clustering.
Architectural Theory: Balancing normalization vs denormalization and natural vs surrogate keys.
Extraction & Logic: Kafka, ANSI SQL, FTP, Apache Spark
Data Formats: JSON, Avro, Parquet
Platforms: Hadoop (HDFS/Hive), Snowflake, Apache Iceberg, Sybase IQ
Demonstrates strong integrity and ethical decision-making.
Acts as a trusted team player collaborating across teams.
Communicates with clarity and confidence.
Works effectively with global teams.
Delivery-focused with strong ownership.
High energy and urgency with professionalism.
Shows intellectual curiosity and continuous improvement.

Requirements

• Education: Bachelor’s or Master’s degree in Computer Science, Applied Mathematics, Engineering, or related field.
• Experience: Minimum 3–5 years of hands-on coding experience; ability to troubleshoot SQL and basic scripting.
• 3+ Yeards of Professional proficiency in Python or Java.
• 3+ Yeards of expeirnce with SDLC, CI/CD best practices and Kubernetes (K8s) deployment experience.
• Temporal Data Modeling: Managing state changes over time (e.g., SCD Type 2).
• Schema Management: Expertise in Schema Evolution (Apache Iceberg) and enforcement strategies.
• Performance Optimization: Knowledge of data partitioning and clustering.
• Architectural Theory: Balancing normalization vs denormalization and natural vs surrogate keys.
• Extraction & Logic: Kafka, ANSI SQL, FTP, Apache Spark
• Data Formats: JSON, Avro, Parquet
• Platforms: Hadoop (HDFS/Hive), Snowflake, Apache Iceberg, Sybase IQ
• Demonstrates strong integrity and ethical decision-making.
• Acts as a trusted team player collaborating across teams.
• Communicates with clarity and confidence.
• Works effectively with global teams.
• Delivery-focused with strong ownership.
• High energy and urgency with professionalism.
• Shows intellectual curiosity and continuous improvement.

Nice To Haves

• Migration experience
• Financial services domain
• Lakehouse experience

Responsibilities

• Engineer will be part of the datastore-migration Factory team responsible for end-to-end datastore migration from on-prem DataLake to AWS hosted LakeHouse.
• Refactoring and migrating extraction logic and job scheduling from legacy frameworks to the new Lakehouse environment.
• Executing the physical migration of underlying datasets while ensuring data integrity.
• Acting as a technical liaison to internal clients, facilitating handoff and sign-off conversations with data owners.
• Translating and optimizing legacy SQL and Spark-based consumption patterns for compatibility with Snowflake and Iceberg.
• Understanding usage patterns to deliver the required data products.
• Working on data reconciliation frameworks to ensure migrated data is functionally equivalent to production data.
• Working with internal data management platforms and learning new workflows and language constructs as necessary.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees