Location:

Toronto, ON Work Model:

Hybrid Job Type:

Full Time Primary Skills

Enterprise Data Architecture, Open-Source Data Platforms, Apache Spark/Kafka Job Description

We are seeking an experienced

Data & Analytics Solutions Architect

with 14+ years of experience in enterprise data architecture, open-source data platforms, distributed data processing, and data engineering. The ideal candidate will lead the design and modernization of scalable, secure, and highly available data platforms supporting batch processing, real-time streaming, analytics, and AI/ML workloads. This role requires deep technical expertise across open-source data technologies, modern lakehouse architectures, data integration, DataOps, and enterprise governance. The successful candidate will collaborate with business and technology stakeholders to define end-to-end architecture, establish technical standards, and deliver production-ready data and AI solutions aligned with business objectives. Key Responsibilities

Engage with business and technology stakeholders to understand data strategy, platform requirements, architecture goals, and transformation priorities. Design and define end-to-end architectures for enterprise open-source data platforms supporting batch, streaming, analytics, and AI workloads. Lead modernization initiatives involving data lakes, lakehouse platforms, distributed processing frameworks, and enterprise data ecosystems. Architect scalable, secure, highly available, and resilient data platforms using open-source technologies and industry best practices. Design data ingestion, integration, streaming, storage, and consumption patterns using Apache Spark, Kafka, Flink, NiFi, and Trino. Develop modern data lakehouse architectures using Apache Iceberg, Hudi, Delta Lake, object storage,



and open table formats. Establish standards for data governance, security, metadata management, lineage, privacy, and observability. Evaluate and recommend open-source technologies based on business requirements, scalability, interoperability, supportability, and total cost of ownership. Define architecture standards, reference architectures, reusable frameworks, CI/CD practices, and DataOps operating models. Provide technical leadership across data modeling, engineering, platform implementation, migration, testing, deployment, and production readiness. Drive platform performance optimization, capacity planning, reliability engineering, failure recovery, and operational excellence. Conduct architecture reviews, create High-Level Design (HLD) and Low-Level Design (LLD) documents, assess non-functional requirements (NFRs), and guide risk mitigation. Support solution design, RFP responses, effort estimation, technical presentations, and client workshops. Assess business use cases and recommend appropriate approaches across BI, rules-based systems, statistical analysis, forecasting, machine learning, and GenAI. Define measurable business KPIs, baselines, acceptance criteria, and success metrics for analytics and AI initiatives. Integrate data, ML, and GenAI reference architectures covering data sources, feature pipelines, models, LLMs, vector stores, serving layers,



and enterprise applications. Support AI/ML and LLM workload deployment on Kubernetes in collaboration with platform and operations teams. Establish MLOps and LLMOps practices, including experiment tracking, model registries, feature stores, automated quality gates, and data/model/prompt versioning. Support AI observability and monitoring for data drift, model drift, LLM quality, tracing, latency, availability, and alerting. Mentor architects and engineers, support technical assessments and hiring, and develop reusable accelerators, playbooks, and engineering best practices. Required Skills & Experience

14+ years of experience in enterprise data architecture, open-source data platforms, distributed data processing, and data engineering. Proven experience designing enterprise solutions using open-source technologies across the end-to-end data-to-insights value chain. Strong experience in architecture definition, HLD/LLD creation, NFR assessment, migration planning, technical governance, and production readiness. Experience leading large-scale data platform modernization and cloud migration initiatives. Open-Source Data Technologies

Hands-on expertise in

Apache Spark, PySpark, Kafka, Flink, Airflow, NiFi, and Trino . Robust programming and query skills in

Python, Scala, Java, and SQL . Experience with

Apache Iceberg, Hudi, Delta Lake , object storage, and open table formats. Knowledge of event-driven architectures, Change Data Capture (CDC), APIs, schema management, and enterprise integration patterns. Experience with PostgreSQL, Cassandra, MongoDB, and OpenSearch, including data modeling, indexing, workload management, and high availability. Strong understanding of batc

📌 Data & Analytics Solutions Architect-6 (Toronto)
🏢 Realign
📍 Toronto