Hadoop Hive Python Developer

Hadoop Hive Python Developer

Location:

Charlotte, NC (Onsite)

Fulltime – Permanent

Job Description

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.

Experience:

Minimum 9+ years

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.

Big Data Platform Engineering

• 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.

Databricks Lakehouse & Delta Framework

• Architect and implement Bronze/Silver/Gold layer modeling patterns within the Databricks Lakehouse.
• Apply Delta Lake best practices including:
• optimized file management
• Z-Ordering
• Delta Change Data Feed (CDF)
• schema evolution & enforcement
• 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.

· 9-14 years of hands-on experience in Big Data engineering.

· Expert skills in:

• PySpark — dataframe optimizations, partitioning, broadcast strategies, distributed computing.
• Kafka — producer/consumer design, schema registry, streaming ETLs.
• Hadoop ecosystem — HDFS, YARN, Map Reduce/Tez, Oozie/Airflow.
• Hive — advanced query tuning, TEZ optimization, partition/bucket management.

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

• Bronze/Silver/Gold layer modeling
• 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.