Technical Lead

Must Have Technical/Functional Skills

• 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
• Cloud & On-Prem Big Data Platforms

Experience Required

Minimum 10+ years

Roles & Responsibilities

Seeking a Senior Big Data Engineer with 10–13 years of experience specializing in Hadoop, PySpark, Kafka, Hive, 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: optimized file management, Z-Ordering, Delta Change Data Feed (CDF), schema evolution & enforcement, and 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

• 10–13 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, MapReduce/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.

Benefits

• Discretionary Annual Incentive
• Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans
• Family Support: Maternal & Parental Leaves
• Insurance Options: Auto & Home Insurance, Identity Theft Protection
• Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement
• Time Off: Vacation, Time Off, Sick Leave & Holidays
• Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing

Pay

Salary Range: $110,000 - $125,000 a year

Qualifications

BACHELOR OF COMPUTER SCIENCE

This response is AI-generated, for reference only.