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.
Experience: Minimum 9+ years
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 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.
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