Hadoop Hive Python Developer

DTEL Engineering & Consultants Inc

Charlotte, United States

7 days ago

Apply on www.dice.com

Prepare application

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

9 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Airflow Data Analysis Big Data Cloud Computing Continuous Integration Data Architecture Data Governance Data Infrastructure Extract Transform Load (ETL) Data Systems Distributed Systems Apache Hadoop

+23 more

Hadoop Distributed File System MapReduce Apache Hive Python (Programming Language) Apache Oozie Scrum Methodology Query Optimization SQL Databases Data Streaming Unstructured Data Workflow Management Systems Cloud Platform System Apache Yarn Delivery Pipeline Git Data Lakes Pyspark Real Time Data Apache Kafka Bitbucket Tez (Software) Jenkins Databricks

Job description

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.

Requirements

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., * 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.