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.
2. Languages: Professional proficiency in Python or Java.
3. Methodology: Deep familiarity with the full Software Development Life Cycle (SDLC) and CI/CD best practices & K8s deployment experience.
4. Core Data Engineering Competencies: Candidates must demonstrate a sophisticated understanding of the following modeling concepts to ensure data correctness during reconciliation:
5. Temporal Data Modeling: Managing state changes over time (e.g., SCD Type 2).
6. Schema Management: Expertise in Schema Evolution (Ref: Iceberg Apache) and enforcement strategies.
7. Performance Optimization: Advanced knowledge of data partitioning and clustering.
8. Architectural Theory: Balancing Normalization vs. Denormalization and the strategic use of Natural vs. Surrogate Keys.
9. 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.