Description

Pipeline Migration Logic & Scheduling

• Refactor and migrate:
  • Extraction logic
  • Job scheduling
• Transition from legacy frameworks to the new Lakehouse environment.

Data Transfer

• Execute physical migration of underlying datasets.
• Ensure:
  • Data integrity
  • Smooth migration process

Stakeholder Engagement

• Act as a technical liaison for internal clients.
• Facilitate:
  • Handoff discussions
  • Sign-off conversations with data owners
• Ensure migrated assets meet business requirements.

Consumption Pattern Migration / Code Conversion

• Translate and optimize:
  • Legacy SQL-based consumption patterns
  • Spark-based consumption patterns
  • Raw and modeled datasets
• Ensure compatibility with:
  • Snowflake
  • Iceberg

Usage Analysis

• Understand data usage patterns.
• Deliver required data products aligned with business needs.

Additional Stakeholder Collaboration

• Partner with data owners and stakeholders throughout migration activities.
• Ensure successful transition and validation of migrated assets.

Data Reconciliation & Quality

• Follow a rigorous approach to data validation.
• Work with reconciliation frameworks to:
  • Validate migrated datasets
  • Build confidence in migrated data quality
• Ensure migrated data is functionally equivalent to existing production data flows.
• Work with additional internal data management platforms.
• Demonstrate aptitude for:
  • Learning new workflows
  • Adapting to new language constructs
  • Supporting evolving platform requirements.

Qualifications:

• Experience ( Relevant) : 6+ years

Top 3 skills which is mandatory

• PySpark,
• Hadoop,
• Data Lakehouse

Education

• Bachelor’s or Master’s degree in:
  • Computer Science
  • Applied Mathematics
  • Engineering
  • Related quantitative field

Experience

• Minimum 3–5 years of professional hands-on coding experience in a collaborative, team-based environment.
• Strong troubleshooting skills in SQL.
• Basic scripting experience required.

Programming Languages

• Professional proficiency in:
  • Python or
  • Java

Methodology

• Deep familiarity with:
  • Full Software Development Life Cycle (SDLC)
  • CI/CD best practices
  • Kubernetes (K8s) deployment experience

Core Data Engineering Competencies

• Candidates should demonstrate a strong understanding of the following concepts to ensure data correctness during reconciliation:

Temporal Data Modeling

• Manage state changes over time
• Example:
  • SCD Type 2

Schema Management

• Expertise in:
  • Schema Evolution
  • Schema enforcement strategies
• Reference:
  • Apache Iceberg

Performance Optimization

• Advanced knowledge of:
  • Data partitioning
  • Data clustering

Architectural Theory

• Understanding of:
  • Normalization vs. Denormalization
  • Natural Keys vs. Surrogate Keys
• Ability to apply the right design strategy based on use cases

Technical Stack Requirements

• While candidates are not expected to be experts in every technology, the collective team should cover:

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 ethical behavior and decision-making.
• Acts as a trusted team player and collaborates effectively across multiple teams and functions.
• Communicates with clarity and confidence through:
  • Concise written communication
  • Structured verbal briefings
  • Proactive stakeholder management
• Works effectively with global teams across:
  • Time zones
  • Cultures
• Builds alignment and resolves issues constructively.
• Delivery-focused with a strong sense of ownership.
• Drives work to completion and consistently meets commitments.
• Brings high energy and urgency while maintaining:
  • Quality
  • Professionalism
• Demonstrates intellectual curiosity by:
  • Asking thoughtful questions
  • Identifying risks early
  • Seeking feedback for continuous improvement

Key Skills

Education

Bachelor's degree