Data & Analytics Solutions Architect

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

Primary Skills

Hadoop Ecosystem, Apache Spark/PySpark, Big Data Architecture

Job Description

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

This role requires robust hands-on expertise in Hadoop, HDFS, YARN, Hive, Spark, Kafka, and distributed data processing, along with the ability to lead architecture definition, platform modernization, migration, and performance optimization initiatives. The successful candidate will collaborate with business stakeholders, data engineers, architects, and platform teams to deliver reliable enterprise data solutions.

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 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.
- 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.
- Develop reusable accelerators, reference architectures, implementation frameworks, and technical best practices.
- Mentor architects and engineers and contribute to hiring, capability development, and knowledge-sharing initiatives.

Required Skills & Experience

Enterprise Data Architecture & Big Data

- 14+ years of experience in Enterprise Data Architecture, Hadoop ecosystem technologies, Big Data Engineering, and Data Platform Modernization.
- Proven 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.
- Experience with architecture definition, HLD/LLD creation, migration planning, technical governance, production readiness, and performance optimization.





Hadoop & Data Engineering Technologies

- Strong hands-on expertise in Hadoop, HDFS, YARN, Hive, Apache Spark, PySpark, Kafka, SQL, Python, Java, and Scala.
- Experience with 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, and Data Governance frameworks.

Security & Governance

- Understanding of Hadoop security and governance controls, including Kerberos, Apache Ranger, encryption, privacy, auditability, compliance, and controlled data access.
- Knowledge of enterprise data security, resilience, and operational requirements.

Leadership & Stakeholder Management

- Experience leading platform modernization, cloud adoption, solution design, and technical implementation.
- Strong architecture governance, problem-solving, communication, and stakeholder management skills.
- Experience supporting client-facing technical discussions, solution estimation, and strategic data platform initiatives.

Domain & Education

- Good understanding of Banking, Financial Services, and Insurance (BFSI) regulatory, security, privacy, resilience, and audit requirements.
- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related discipline, or equivalent relevant experience.

Preferred Skills
- Certifications in Hadoop, Spark, Cloud Data Engineering, Enterprise Architecture, Data Management, Security, or Big Data Platforms.
- Experience with cloud-based Big Data platforms and large-scale data migration.
- Knowledge of modern data platform architecture and emerging data engineering practices.

📌 Data & Analytics Solutions Architect (Toronto)
🏢 AceStack
📍 Toronto