Data Modeler

Responsibility:
Stream & Batch Modeling: Design conceptual, logical, and physical data models for streaming data platforms using Kafka and Flink
Distributed Processing Architecture: Architect data structures optimized for Spark batch processing and in-memory event streaming, ensuring data integrity and low latency.
NoSQL Data Modeling: Design flexible schemas, indexes, and aggregation pipelines in MongoDB to support high-speed, unstructured, and semi-structured application data.
Data Governance & Metadata: Establish data governance standards, metadata management, and data lineage for both event streams and historical data stores.
Performance Optimization: Collaborate with data engineers to tune data models for high-throughput, high-volume workloads and distributed computing constraints
Cross-functional Collaboration: Partner with software developers, data scientists, and business analysts to translate business use cases into scalable data strategies.

Qualification:
Experience: 5 to 8+ years of experience in data modeling, data warehousing, or big data architecture.
Core Technologies: Proven ability to model, design, and work with distributed data ecosystems:
Kafka: Designing event-driven architecture, topics, schemas, and event payloads.
Flink & Spark: Structuring data for stateful stream processing and large-scale batch ETL pipelines.
MongoDB: Document modeling, indexing strategies, and querying nested data structures.
Programming: Proficiency in SQL along with coding experience in Python, Java, or Scala.
Modeling Tools: Familiarity with modern data modeling and visualization tools (e.g., Hackolade, erwin).

Education: Bachelor's degree in Computer Science, Data Engineering, Information Technology, or a related field



Behavioral Skills:
Good Communication skills
Flexible to rotational shifts, 5 days WFO
Team Player
Ability to work in a changing environment
Strong problem solving and analytical skills
Ability to work independently or within a team
Manage day-to-day challenges and communicate developmental risks with the technical team