Technical Architect

Primary 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, and Cloud & On-prem Big Data Platforms.

Experience:

Minimum 10+ years

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.

• 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.
• Architect and implement Bronze/Silver/Gold layer modeling patterns within the Databricks Lakehouse.
• Apply Delta Lake best practices including:
  • Optimized file management
  • Z‑Ordering
• 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.
• Collaborate closely with quants, product owners, architects, risk tech, and business users.
• Mentor junior engineers and contribute to building strong engineering practices across tech teams.

• 10–13 years of hands‑on experience in Big Data engineering.
• PySpark — dataframe optimizations, partitioning, broadcast strategies, distributed computing.
• Hive — advanced query tuning, TEZ optimization, partition/bucket management.
• Extensive hands‑on experience with Databricks Lakehouse, including:
  • 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.

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. Salary Range: $,000 a year.

• Bachelor of Computer Science or equivalent.