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About
Data Engineer
with
5+ years of experience
with strong hands‑on experience in pipeline migration, consumption pattern migration, data reconciliation, and quality validation. The ideal candidate should have practical experience working with
Snowflake, Apache Iceberg, Spark, Kafka, SQL, and large‑scale data engineering platforms, along with strong understanding of SDLC, CI/CD, and data modeling concepts.
This is a full time opportunity.
Job Location
Employment Type
Full Time / Hybrid
Experience
5+ Years
Key Responsibilities • Pipeline Migration – Refactoring and migrating extraction logic and job scheduling from legacy frameworks to the new Lakehouse environment.
• Pipeline Migration – Executing the physical migration of underlying datasets while ensuring data integrity.
• Pipeline Migration – 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 – Translating and optimizing legacy SQL and Spark‑based consumption patterns (raw and modeled) for compatibility with Snowflake and Iceberg.
• Consumption Pattern Migration – Understand usage patterns to deliver the required data products.
• Consumption Pattern Migration – 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 – Work with reconciliation frameworks to build confidence that migrated data is functionally equivalent to that already used within production flows.
Technical Skills:
Experience:
Minimum of 5 years of professional "hands‑on‑keyboard" coding experience in a collaborative, team‑based environment. Ability to troubleshoot (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:
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.
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Languages
- English
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