Data Engineer

JOB SUMMARY We are seeking a highly motivated Engineer to join our Datastore Migration Factory team. This critical and high-visibility project involves the end-to-end migration of our on-premise Data Lake to an AWS-hosted Lakehouse. The Engineer will play a crucial role in migrating pipelines and consumption patterns, ensuring data integrity and quality, and acting as a technical liaison with stakeholders. The successful candidate will possess strong data engineering skills, experience with data migration, and excellent communication and collaboration abilities. This is a 100% onsite position based in Dallas, TX; local candidates only will be considered. Key Responsibilities 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. Translating and optimizing legacy SQL and Spark-based consumption patterns (raw and modeled) for compatibility with Snowflake and Iceberg. Performing usage analysis to understand usage patterns and deliver required data products. Working with reconciliation frameworks to validate migrated data and ensure functional equivalence. Acting as a technical liaison to internal clients, facilitating "handoff and sign-off" conversations with data owners. Collaborating effectively across multiple teams and functions. Communicating concise written updates, structured verbal briefings, and proactively managing stakeholders. Required Qualifications Bachelor’s or Master’s degree in Computer Science, Applied Mathematics, Engineering, or a related quantitative field. 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. Professional proficiency in Python or Java. Deep familiarity with the full Software Development Life Cycle (SDLC). Experience with CI/CD best practices & K8s deployment. Sophisticated understanding of temporal data modeling (e.g., SCD Type 2). Expertise in Schema Evolution (Ref: Iceberg Apache) and enforcement strategies. Advanced knowledge of data partitioning and clustering. Understanding of balancing Normalization vs. Denormalization and the strategic use of Natural vs. Surrogate Keys. Experience with data extraction and logic tools such as Kafka, ANSI SQL, FTP, and Apache Spark. Experience with data formats such as JSON, Avro, and Parquet. Experience with platforms such as Hadoop (HDFS/Hive), Snowflake, Apache Iceberg, and Sybase IQ. Demonstrated integrity and ethical decision‑making. Delivery‑focused with a strong sense of ownership. Strong intellectual curiosity and problem‑solving skills. Ability to work effectively with global teams across time zones and cultures. #J-18808-Ljbffr Compunnel