Data & Analytics Solutions Architect-6

Role overview

Location and work model

• The posting specifies Toronto, Ontario, and an onsite work model.

Key responsibilities

• Engage business and technology stakeholders to understand objectives, platform requirements, and modernization goals.
• Define end-to-end Hadoop and Big Data platform architecture for analytics, reporting, and AI workloads.
• Design Hadoop ecosystems using HDFS, YARN, Spark, Kafka, Hive, and related technologies.
• Architect data ingestion, batch processing, streaming, and Data Lake solutions.
• Optimize distributed processing with Spark, Hive, HBase, and MapReduce.
• Define data models, Data Lake zones, governance, metadata, lineage, and security standards.
• Lead platform modernization, migrations, and optimization initiatives.
• Establish architecture standards, reusable frameworks, engineering practices, and DataOps operating models.
• Create high- and low-level designs and guide implementation teams through delivery.
• Lead technical work across design, development, testing, migration, deployment, and production support.
• Conduct architecture reviews, identify technical risks and dependencies, and recommend mitigations.
• Drive performance tuning, capacity planning, reliability, workload optimization, and infrastructure cost efficiency.

Required skills and experience

• 14+ years in Enterprise Data Architecture, Hadoop ecosystems, Big Data Engineering, and Data Platform Modernization.
• Hands-on expertise in Hadoop, HDFS, YARN, Hive, Spark, PySpark, Kafka, SQL, Python, Java, and Scala.
• Experience designing large-scale Big Data and Data Lake architectures for analytics, reporting, and AI workloads.
• Knowledge of Hadoop high availability, cluster management, workload optimization, and scalability.
• Expertise in distributed processing with Spark, Hive, HBase, and MapReduce, and in Kafka-based ingestion and streaming.
• Knowledge of ETL/ELT, integration, data quality, metadata, lineage, observability, and DataOps.
• Experience with data warehousing, enterprise data models, Master Data Management, and data governance.
• Understanding of Kerberos, Apache Ranger, encryption, privacy, auditability, compliance, and controlled data access.
• Experience with architecture definition, HLD/LLD creation, migration planning, technical governance, production readiness, and performance optimization.
• Experience supporting modernization, cloud adoption, solutioning, estimation, and client-facing technical discussions.
• BFSI experience and related regulatory, security, and compliance knowledge are highly desirable.