We are seeking a highly proficient and results-driven Data Engineering Architect with a robust background in designing, implementing, and maintaining scalable and resilient data ecosystems.
The ideal candidate will possess a minimum of five years of dedicated experience in orchestrating complex data workflows and will serve as a key contributor within our advanced data services team.
This role requires a meticulous professional who can transform intricate business requirements into high-performance, production-grade data solutions.
Key Responsibilities:
- Architectural Stewardship: Design, develop, and optimize sophisticated data pipelines leveraging distributed computing frameworks to ensure efficiency, reliability, and scalability of data ingestion, transformation, and delivery layers.
- SQL Mastery: Act as a subject matter expert in SQL, crafting and refining highly complex, multi-layered queries and stored procedures for advanced data manipulation, extraction, and reporting, while ensuring optimal performance and resource utilization.
- Distributed Processing Expertise: Lead the development and deployment of data processing jobs using Apache Spark , orchestrating complex data transformations, aggregations, and feature engineering at petabyte-scale.
- Big Data Orchestration: Proactively manage and evolve our data warehousing solutions built on Apache Hive , overseeing schema design, partition management, and query optimization to support large-scale analytical and reporting needs.
- Collaborative Innovation: Work synergistically with senior engineers and cross-functional teams to conceptualize and execute architectural enhancements, data modeling strategies, and systems integrations that align with long-term business objectives.
- Quality Assurance & Governance: Establish and enforce rigorous data quality standards, implementing comprehensive validation protocols and monitoring mechanisms to guarantee data integrity, accuracy, and lineage across all systems.
- Operational Excellence: Proactively identify, diagnose, and remediate technical bottlenecks and anomalies within data workflows, ensuring system uptime and operational stability through systematic troubleshooting and root cause analysis.
Required Competencies & Qualifications:
- Educational Foundation: Bachelor's degree in Computer Science, Information Systems, or a closely related quantitative field.
- Experience: A minimum of five (5) years of progressive, hands-on experience in a dedicated data engineering or equivalent role, with a proven track record of delivering enterprise-level data solutions.
- Core Technical Skills:
SQL: Expert-level proficiency in SQL programming is non-negotiable, including advanced query optimization, window functions, and schema design principles.
Distributed Computing: Demonstrated high-level proficiency with Apache Spark for large-scale data processing.
Data Warehousing: In-depth, practical experience with Apache Hive and its ecosystem.
- Conceptual Knowledge: Deep understanding of data warehousing methodologies, ETL/ELT processes, and dimensional modeling.
- Analytical Acumen: Exceptional problem-solving and analytical capabilities, with the ability to dissect complex technical challenges and formulate elegant, scalable solutions.
- Continuous Learning: A relentless curiosity and a strong desire to stay abreast of emerging technologies and industry best practices.
- Domain Preference: Prior professional experience within the Banking or Financial Services sector is highly advantageous.