Hadoop Developer

Primary skills

PySpark, Apache Kafka, Hadoop Ecosystem, Hive, Databricks Lakehouse Architecture, Delta Lake, Bronze/Silver/Gold Data Modeling, Big Data ETL Pipeline Development, SQL, Real‑time Data Ingestion Frameworks, Data Governance & Cataloging, CI/CD Tools – Git, Jenkins, Bitbucket, Workflow Orchestration, and Cloud & On‑Prem Big Data Platforms.

Seeking a Senior Big Data Engineer with 10‑13 years of experience specializing in Hadoop, PySpark, Kafka, Hive, and strong experience designing data solutions for large‑scale financial systems.

In addition, the candidate must possess advanced expertise in Databricks Lakehouse architecture, particularly around Bronze/Silver/Gold layer data modeling, Delta Lake optimizations, and building reliable, scalable pipelines for regulatory, risk, trading, and analytics workloads.

This role focuses on delivering highly performant, well‑governed data platforms that support the bank’s mission‑critical global markets functions.

• Design, develop, and optimize PySpark‑based ETL pipelines running on on‑prem Hadoop clusters and cloud environments.
• Build high‑volume ingestion frameworks using Kafka for real‑time and near‑real‑time trading and market data.
• Develop, tune, and manage Hadoop ecosystem components—HDFS, YARN, Map Reduce, Tez, Oozie/Airflow.
• Build high‑performance, optimized Hive data models for regulatory reporting, trade lifecycle, and market risk processing.
• Architect and implement Bronze/Silver/Gold layer modeling patterns within the Databricks Lakehouse.
• Apply Delta Lake best practices including:
  • optimized file management
  • Z‑Ordering
  • Build reusable frameworks for ingestion, cleansing, transformation, and consumption of data across Lakehouse layers.
• Enable governance, lineage, and auditability using Unity Catalog or equivalent cataloging tools.
• Collaborate closely with quants, product owners, architects, risk tech, and business users.
• Mentor junior engineers and contribute to building strong engineering practices across tech teams.

• 10‑13 years of hands‑on experience in Big Data engineering.
• Expert skills in:
  • PySpark — dataframe optimizations, partitioning, broadcast strategies, distributed computing.
  • Hive — advanced query tuning, TEZ optimization, partition/bucket management.
• Extensive hands‑on experience with Databricks Lakehouse, including:
  • Delta Lake optimizations
  • Data quality frameworks on Lakehouse
  • Structured & unstructured data handling
• Experience in Global Markets, Risk, Treasury, Trade Surveillance, or Regulatory Reporting.
• Strong SQL knowledge with experience working on massive datasets (TB/PB scale).
• Experience with CI/CD practices — Git, Jenkins, Bitbucket, build pipelines.