SDE II - Data

Requirements:
4+ years of data engineering experience, with at least 2 years in a lead or senior technical role.
Experience building and scaling streaming data pipelines in large-scale, distributed environments.
Strong skills in Python, Java and SQL, with expert-level skill in either Python or Java.
Proven experience building streaming data pipelines (e. g., Kafka, Flink, Spark, and Kinesis).
Experience with embedding pipelines and vector stores (e. g., Pinecone, Weaviate, FAISS, and pgvector).
Strong knowledge of data modelling, storage optimisation, and retrieval patterns for large-scale systems.
Hands-on experience with workflow orchestration tools (Airflow, Dagster, etc. ).
Strong collaboration and communication skills, able to partner across AI engineering, infra, and product teams.
Familiarity with testing, monitoring, and automation for data pipelines.
Preferred Qualifications:
Experience integrating AI-ready data stores with LLM orchestration frameworks (LangChain, LangGraph, etc. ).
Knowledge of observability and monitoring stacks (Datadog, Prometheus, or equivalent).
Background in governance and compliance practices for enterprise data platforms.
Experience building data frameworks, registries, or accelerators adopted by multiple teams.
Experience ensuring data security, lineage, and auditability in enterprise data environments.
Skilled at writing design documentation, driving system architecture reviews, and influencing data engineering culture.
Experience with Python, Java, Databricks, LangChain, Vector Stores (Pinecone, Weaviate, FAISS, and pgvector), SQL, and AWS big data tech stack (like S3 Glue, and MWAA).

Experience
5-9 yrs