Data Engineer (Data Lake to AWS Migration) - Dallas Onsite - 10+ yrs

Role: Data Engineer

Location: Dallas, TX (5 days Onsite)

Exp: 10+ Yrs

Top Skills: DataLake, AWS, Migration, Spark, Python/Java, Hadoop, Kafka

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 Data Lake to AWS hosted LakeHouse.

Responsibilities of the Engineer include:

1. Pipeline Migration:

1. Logic & Scheduling: Refactoring and migrating extraction logic and job scheduling from legacy
   frameworks to the new Lakehouse environment.
2. Data Transfer: Executing the physical migration of underlying datasets while ensuring data integrity.
3. 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.

1. Consumption Pattern Migration:

1. Code Conversion: Translating and optimizing legacy SQL and Spark-based consumption patterns (raw
   and modeled) for compatibility with Snowflake and Iceberg.
2. Usage analysis: Understand usage patterns to deliver the required data products.
3. 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.
4. Data Reconciliation & Quality

1. 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.

Basic Qualifications:

1. Education: Bachelor s or Masters in Computer Science, Applied Mathematics, Engineering, or a related quantitative field.
2. 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.
3. Languages: Professional proficiency in Python or Java.
4. Methodology: Deep familiarity with the full Software Development Life Cycle (SDLC) and CI/CD best practices & K8s deployment experience

Technical Stack Requirements: Kafka, ANSI SQL, FTP, Apache Spark, JSON, Avro, Parquet, Hadoop (HDFS/Hive), Snowflake, Apache Iceberg, Sybase IQ

Regards,

pradeep(@)smg-llc(.)us