Hybrid Data Engineer

at Bounteous

• Job title: Data Engineer
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: $110,000 - $125,000 per year
• Degree requirement: Bachelor's Degree
• Tech skills: Apache, Hadoop, HDFS, Java, Kafka, Kubernetes, Python, SDLC, Spark, SQL
• Location requirements: Hybrid, Dallas, United States

About the role

• Data Engineer specializing in digital transformation at Bounteous. Collaborating on data solutions and migration projects with a focus on data integrity and quality.

Responsibilities

• 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.