Data Engineer

Responsibilities of the Engineer include:

1. Pipeline Migration

a.

Logic & Scheduling: Refactoring and migrating extraction logic and job scheduling from legacy

frameworks to the new Lakehouse environment.

b.

c.

Data Transfer: Executing the physical migration of underlying datasets while ensuring data integrity.

Stakeholder Engagement: Acting as a technical liaison to internal clients, facilitating "hand-off and

sign-off" conversations with data owners to ensure migrated assets meet business requirements.

2. Consumption Pattern Migration

a. Code Conversion: Translating and optimizing legacy SQL and Spark-based consumption patterns (raw and modeled) for compatibility with Snowflake and Iceberg.

b. Usage analysis: Understand usage patterns to deliver the required data products.

c. Stakeholder Engagement: Acting as a technical liaison to internal clients, facilitating "hand-off and

d. sign-off" conversations with data owners to ensure migrated assets meet business requirements.

Data Reconciliation & Quality

3. 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.

Engineer will also need to work with internal data management platforms team and must have an aptitude for

learning new workflows and language constructs as necessary.

Technical Skills:

1. Basic Qualifications

A. Education: Bachelors or Masters in Computer Science, Applied Mathematics, Engineering, or a

related quantitative field.

B. Experience: Minimum of 3-5 years of professional "hands-on-keyboard" coding experience in a

collaborative, team-based environment. Ability to trouble shoot (SQL) and basic scripting

experience.

c.Languages: Professional proficiency in Python or Java.

d.Methodology: Deep familiarity with the full Software Development Life Cycle (SDLC) and CI/CD best

practices & K8s deployment experience.

2. Core Data Engineering Competencies: Candidates must demonstrate a sophisticated understanding of

the following modeling concepts to ensure data correctness during reconciliation:

A. Temporal Data Modeling: Managing state changes over time (e.g., SCD Type 2).

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

strategies.

c.Performance Optimization: Advanced knowledge of data partitioning and clustering.

d.Architectural Theory: Balancing Normalization vs. Denormalization and the strategic use of Natural

vs. Surrogate Keys.

3. Technical Stack Requirements: While candidates are not expected to be experts in every tool, the collective team must cover the following technologies:

Extraction & Logic

Kafka, ANSI SQL, FTP, Apache Spark

Data Formats

JSON, Avro, Parquet

Platforms

Hadoop (HDFS/Hive), Snowflake, Apache Iceberg, Sybase IQ

Core Competencies:

• Demonstrates strong integrity and consistently models good conduct and ethical decision-making.
• Acts as a trusted team player who collaborates effectively across multiple teams and functions.
• Communicates with clarity and confidence - concise written updates, structured verbal briefings, and

proactive stakeholder management.

• Works effectively with global teams across time zones and cultures; builds alignment and resolves issues

constructively.

• Delivery-focused with a strong sense of ownership; drives work to closure and meets commitments.
• Brings high energy and urgency to achieve targets while maintaining quality and professionalism.
• Shows intellectual curiosity; asks thoughtful questions, surfaces risks early, and seeks feedback to

continuously improve.