Data & Analytics Solutions Architect

Job Title: Data & Analytics Solutions Architect

Location: Toronto, Onsite

Job Type: Full-Time Permanent

Must HaveCDP (Hadoop)

Skills Required:

Must to Have:

• 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, Spark, PySpark, Kafka, SQL, Python, Java, and Scala.

• Experience designing and implementing large-scale Big Data and Data Lake architectures supporting analytics, reporting, and AI workloads.

• Strong understanding of Hadoop platform architecture, including HDFS, YARN, high availability, cluster management, workload optimization, and scalability.

• 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, Spark, PySpark, Kafka, SQL, Python, Java, and Scala.

• Experience designing and implementing large-scale Big Data and Data Lake architectures supporting 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 frameworks.

• Experience building data ingestion and streaming solutions using Kafka and Hadoop ecosystem tools.

• Strong knowledge of Data Engineering practices, including ETL/ELT, data integration, data quality, metadata management, lineage, observability, and DataOps.

• Experience with Data Lake, Data Warehousing, Enterprise Data Models, Master Data, and Data Governance frameworks.

• Strong understanding of security and governance controls, including Kerberos, 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 certifications in Hadoop, Spark, Cloud Data Engineering, Enterprise Architecture, Data Management, Security, or Big Data Platforms.

Roles and 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 data ingestion, batch processing, streaming, and data lake solutions for enterprise-scale environments.

• Define data models, data lake zones, governance frameworks, metadata management, lineage, and security standards.

• Lead Hadoop modernization, migration, and platform optimization initiatives to improve performance, scalability, and operational efficiency.

• Establish architecture standards, reusable frameworks, engineering best practices, and DataOps operating models.

• Provide technical leadership across solution design, implementation, testing, migration, deployment, and production support activities.

• Conduct architecture reviews, identify risks and dependencies, and guide teams on mitigation strategies and best practices.

• Drive performance tuning, capacity planning, reliability improvements, and infrastructure cost optimization.

• 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 initiatives.

Develop reusable accelerators, reference architectures, implementation frameworks, and technical best practices for Big Data platforms