Data Engineer

Blutic is seeking a skilled

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

Dallas, TX

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:

1. 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.

1. Languages:

Professional proficiency in

Python

or

Java

.

1. 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:

1. Temporal Data Modeling:

Managing state changes over time (e.g., SCD Type 2).

1. Schema Management:

Expertise in Schema Evolution (Ref: Iceberg Apache) and enforcement strategies.

1. Performance Optimization:

Advanced knowledge of data partitioning and clustering.

1. 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.