Big Data Engineer(Hadoop, Python, Hive, PySpark)

Role :: Big Data Engineer(Hadoop, Python, Hive, PySpark)

Location :: Charlotte, NC - Onsite 3 days a week

Fulltime_ Permanent Role

Job Description

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 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, 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 — dataframe 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.