Data Engineer

Information Security Responsibilities

• Promote and enforce awareness of key information security practices, including acceptable use of information assets, malware protection, and password security protocols
• Identify, assess, and report security risks, focusing on how these risks impact the confidentiality, integrity, and availability of information assets
• Understand and evaluate how data is stored, processed, or transmitted, ensuring compliance with data privacy and protection standards (GDPR, CCPA, etc.)
• Ensure data protection measures are integrated throughout the information lifecycle to safeguard sensitive information

Responsibilities:

1. 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 sign-off" conversations with data owners to ensure migrated assets meet business requirements
1. 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 "handoff and sign-off" conversations with data owners to ensure migrated assets meet business requirements.
1. 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.

Technical Skills:

1. Basic Qualifications

• Education: Bachelor’s or Master’s degree in Computer Science, Applied Mathematics, Engineering, or a related quantitative field.
• Experience: Minimum of 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.
1. 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.
1. Technical Stack Requirements: 

Extraction & Logic: Kafka, ANSI SQL, FTP, Apache Spark
Data Formats: JSON, Avro, Parquet
Platforms: Hadoop (HDFS/Hive), Snowflake, Apache Iceberg, Sybase IQ

Candidate will also need to work with our internal data management platform, and must have an aptitude for learning new workflows and language constructs is essential.