Data Architect

Job Summary

Senior Data Architect

Key Responsibilities

• Architect and govern

  enterprise Big Data platforms

  (data lake, lakehouse, warehouse, real-time).
• Design

  high-volume, high-velocity data pipelines

  using batch and streaming frameworks.
• Lead implementation of

  distributed processing architectures

  (Spark, PySpark, EMR).
• Build

  event-driven and real-time streaming solutions

  (Kafka, Kinesis, Flink).
• Define

  ETL/ELT patterns

  , metadata-driven pipelines, and reusable ingestion frameworks.
• Drive

  data platform automation

  (Airflow/Step Functions, CI/CD, data quality, observability).
• Optimize

  performance, scalability, fault tolerance, and cost

  across Big Data workloads.
• Integrate

  GenAI architectures

  (LLMs, embeddings, vector databases, RAG) with enterprise data lakes.
• Ensure

  security, governance, lineage, and compliance

  across data platforms.
• Provide hands-on leadership and technical mentoring to data engineering teams.

Required Technical Skills & Experience

• 12+ years

  in

  Big Data Engineering / Data Architecture

  roles.
• Expert-level experience with

  Spark, PySpark, SQL

  , and distributed compute engines.
• Strong knowledge of

  AWS Big Data stack

  : S3, EMR, Glue, Athena, Redshift, Lambda, Step Functions.
• Hands-on experience with

  Snowflake

  (performance tuning, data sharing, optimization).
• Expertise in

  streaming platforms

  : Kafka, Kinesis, Flink, or Spark Streaming.
• Strong experience with

  data modeling

  (dimensional, Data Vault 2.0).
• Proficiency in

  Python

  , schema evolution, partitioning, and data versioning.
• Experience with

  orchestration and automation

  tools (Airflow, Dagster, CI/CD).
• Working knowledge of

  GenAI data integration

  (feature stores, vector DBs, RAG pipelines).
• Experience with

  Agile delivery

  and leading globally distributed engineering teams.