Description

You design and optimize data schemas for streaming and NoSQL ecosystems.

This role is on-site.

Responsibilities

• Design conceptual, logical, and physical data models for streaming platforms using Kafka and Flink.
• Architect data structures for Spark batch processing and in-memory event streaming to ensure low latency.
• Create flexible schemas, indexes, and aggregation pipelines in MongoDB for high-speed unstructured data.
• Establish data governance standards, metadata management, and data lineage for event streams and historical stores.
• Tune data models for high-throughput workloads and distributed computing constraints alongside data engineers.

Required Skills

• 5+ years of experience in data modeling, data warehousing, or big data architecture.
• Proficiency in SQL and coding experience in Python, Java, or Scala.
• Experience designing event-driven architecture, topics, schemas, and event payloads in Kafka.
• Ability to structure data for stateful stream processing (Flink) and large-scale batch ETL (Spark).
• Knowledge of document modeling, indexing strategies, and querying nested data structures in MongoDB.
• Familiarity with modern data modeling tools such as Hackolade or erwin.
• Strong understanding of distributed data ecosystems and data integrity principles.

Preferred Skills

• Experience collaborating with data scientists and business analysts to translate use cases into scalable data strategies.
• Background in real-time ingestion and stream processing architectures.

Key Skills

Education

Any Graduate