Data Engineer (Data Lake to AWS Migration)

Systems, Inc

Dallas, United States of America

9 days ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Languages

English

Experience level

Intermediate

Job location

Dallas, United States of America

Tech stack

Java

Apache HTTP Server

Computer Programming

Continuous Integration

Data Validation

Data Transmissions

Data Integrity

Hadoop

Hadoop Distributed File System

Hive

JSON

Job Scheduling

Python

SQL Databases

Usage Analysis

Parquet

Scripting (Bash/Python/Go/Ruby)

File Transfer Protocol (FTP)

Snowflake

Spark

SAP Sybase ASE

Ansi Sql

Kubernetes

Information Technology

Avro

Kafka

Code Restructuring

Job description

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 "hand-off and sign-off" 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.
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.
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.

Requirements

Education: Bachelor s or Masters in Computer Science, Applied Mathematics, Engineering, or a related quantitative field.
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.
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

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