Hadoop Hive Python Developer

Must Have Technical/Functional Skills

Primary skills: Hadoop, Hive, Python, 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.

Roles & Responsibilities

Seeking a Senior Big Data Engineer with 9-14 years of experience specializing in Hadoop, Python, Hive PySpark, Kafka, 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.

Key Responsibilities:

Big Data Platform Engineering

• Design, develop, and optimize PySpark-based ETL pipelines running on onprem Hadoop clusters and cloud environments.
• Build highvolume ingestion frameworks using Kafka for real-time and near-real-time trading and market data.
• Develop, tune, and manage Hadoop ecosystem components HDFS, YARN, MapReduce, Tez, Oozie/Airflow.
• Build high-performance, optimized Hive data models for regulatory reporting, trade lifecycle, and market risk processing.

Databricks Lakehouse & Delta Framework

• Architect and implement Bronze/Silver/Gold layer modeling patterns within the Databricks Lakehouse.
• Apply Delta Lake best practices including:

o optimized file management

o Z-Ordering

o Delta Change Data Feed (CDF)

o schema evolution & enforcement

o ACID transaction handling

• 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.

Collaboration, Leadership & Delivery

• Collaborate closely with quants, product owners, architects, risk tech, and business users.
• Participate in agile ceremonies sprint planning, refinement, design reviews.
• Mentor junior engineers and contribute to building strong engineering practices across tech teams.

Required Skills & Experience

• 9-14 years of hands-on experience in Big Data Engineering.
• Expert skills in:

o PySpark data frame optimizations, partitioning, broadcast strategies, distributed computing.

o Kafka producer/consumer design, schema registry, streaming ETLs.

o Hadoop ecosystem HDFS, YARN, MapReduce/Tez, Oozie/Airflow.

o Hive advanced query tuning, TEZ optimization, partition/bucket management.

• Extensive hands-on experience with Databricks Lakehouse, including:

o Bronze/Silver/Gold layer modeling

o Delta Lake optimizations

o Data quality frameworks on Lakehouse

o 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.