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.
- Robust 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.
- Strong 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 Dat

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