Engineer

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 banks 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 componentsHDFS, 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.

TCS Employee Benefits Summary:


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: $100,000-$120,000 a year