Job Type: Full Time

Job Category: IT

Job Description

Data & Analytics Solutions Architect

Primary Skills: Hadoop Ecosystem & HDFS/YARN, Apache Spark/PySpark, Big Data Architecture & Data Lake Solutions

Location: Toronto, ON
Work Model: Onsite
Job Type: Full Time

Job Description

We are seeking an experienced Data & Analytics Solutions Architect with strong expertise in Enterprise Data Architecture, Hadoop ecosystems, Big Data Engineering, and Data Platform Modernization. The ideal candidate will have 14+ years of experience designing and implementing scalable, secure, and high-performance data platforms that support enterprise analytics, reporting, and AI workloads.

The role requires hands-on knowledge of Hadoop, HDFS, YARN, Spark, PySpark, Hive, Kafka, and distributed data processing, combined with the ability to define end-to-end architecture, lead modernization initiatives, establish governance standards, and provide technical leadership across large-scale enterprise data programs. Experience in the BFSI domain and knowledge of regulatory, security, and compliance requirements are highly desirable.

Key Responsibilities

• Engage with business and technology stakeholders to understand business objectives, data platform requirements, and modernization goals.
• Define end-to-end architecture for Hadoop and Big Data platforms supporting analytics, reporting, and AI workloads.
• Design scalable, secure, and high-performance Hadoop ecosystems using HDFS, YARN, Spark, Kafka, Hive, and related technologies.
• Architect enterprise-scale data ingestion, batch processing, streaming, and Data Lake solutions.
• Design and optimize distributed data processing solutions using Spark, Hive, HBase, and MapReduce.
• Define data models, Data Lake zones, governance frameworks, metadata management, data lineage, and security standards.
• Lead Hadoop platform modernization, migration, and optimization initiatives to improve performance, scalability, and operational efficiency.
• Establish architecture standards, reusable frameworks, engineering best practices, and DataOps operating models.
• Create High-Level Designs (HLDs) and Low-Level Designs (LLDs) and guide implementation teams throughout the delivery lifecycle.
• Provide technical leadership across solution design, development, testing, migration, deployment, and production support.
• Conduct architecture reviews, identify technical risks and dependencies, and recommend mitigation strategies.
• Drive performance tuning, capacity planning, platform reliability, workload optimization, and infrastructure cost efficiency.
• Ensure appropriate security and governance controls, including access management, encryption, privacy, auditability, and regulatory compliance.
• Collaborate with data engineers, platform teams, architects, vendors, and client stakeholders to deliver enterprise data solutions.
• Support solutioning, RFP responses, effort estimation, technical presentations, and client workshops.
• Mentor architects and engineers and contribute to hiring, capability development, and knowledge sharing.
• Develop reusable accelerators, reference architectures, implementation frameworks, and technical best practices for Big Data platforms.

Required Skills & Experience

• 14+ years of experience in Enterprise Data Architecture, Hadoop Ecosystem, Big Data Engineering, and Data Platform Modernization.
• Strong hands-on expertise in Hadoop, HDFS, YARN, Hive, Apache Spark, PySpark, Kafka, SQL, Python, Java, and Scala.
• Experience designing and implementing large-scale Big Data and Data Lake architectures for analytics, reporting, and AI workloads.
• Strong understanding of Hadoop platform architecture, including HDFS, YARN, high availability, cluster management, workload optimization, and scalability.
• Expertise in batch and distributed data processing using Spark, Hive, HBase, and MapReduce.
• Experience building data ingestion and streaming solutions using Kafka and Hadoop ecosystem tools.
• Strong knowledge of ETL/ELT, data integration, data quality, metadata management, data lineage, observability, and DataOps.
• Experience with Data Lakes, Data Warehousing, Enterprise Data Models, Master Data Management, and Data Governance frameworks.
• Understanding of security and governance controls, including Kerberos, Apache Ranger, encryption, privacy, auditability, compliance, and controlled data access.
• Proven experience in architecture definition, HLD/LLD creation, migration planning, technical governance, production readiness, and performance optimization.
• Experience supporting platform modernization, cloud adoption, solutioning, effort estimation, and client-facing technical discussions.
• Good understanding of BFSI regulatory, security, privacy, resilience, and audit requirements.

Preferred Qualifications

• Certifications in Hadoop, Spark, Cloud Data Engineering, Enterprise Architecture, Data Management, Security, or Big Data Platforms.
• Experience with enterprise-scale Hadoop modernization and cloud migration programs.
• Strong stakeholder management, technical communication, and problem-solving skills.
• Ability to mentor technical teams and establish reusable architecture and engineering practices.

Required Skills