Senior Data Architect

2 days ago


Jersey City, New Jersey, United States EXL Full time

About the Role

We are seeking a
Senior Data Architect
with deep
Big Data Engineering expertise
to design and modernize
large-scale, cloud-native data platforms
. This role emphasizes
distributed data processing, real-time pipelines, data platform automation
, and
GenAI enablement
on top of strong Big Data foundations.

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



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