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
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.
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 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.
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.
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.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

SQLPythonJavaData MigrationData ValidationData ModelingPerformance OptimizationSchema ManagementTemporal Data ModelingCode Conversion

Soft Skills

Stakeholder EngagementCollaborationTroubleshootingCommunication