Inclusion without Exception
Tata Consultancy Services (TCS) is an equal opportunity employer, and embraces diversity in race, nationality, ethnicity, gender, age, physical ability, neurodiversity, and sexual orientation, to create a workforce that reflects the societies we operate in. Our continued commitment to Culture and Diversity is reflected in our people stories across our workforce and implemented through equitable workplace policies and processes.
TCS has been serving the Canadian marketplace for more than 35 years and today is the technology partner of choice for many of the country's leading organizations. As one of the largest IT services providers in Canada and globally, TCS is aiming to become the world's largest AI-led technology services company and is enabling its clients to transform themselves across the full AI stack, from infrastructure to intelligence.
Rooted in the heritage and values of the Tata Group, TCS is focused on creating long-term value for its clients, investors, employees, and the communities it serves. With a highly skilled workforce supporting customers from coast to coast, TCS has consistently been recognized as a Top Employer by the Top Employers Institute, including a recent top ranking at the country level.
TCS also sponsors 14 of the world's most prestigious marathons and endurance events, including the TCS Toronto Waterfront Marathon, reflecting its commitment to health, sustainability, and community empowerment.
Job Description :-
Skills Required:
- Senior years of experience in Enterprise Data Architecture, Open-Source Data Platforms, Distributed Data Processing, and Data Engineering.
- Proven experience in Enterprise Solution architecture using Open source technology stack across the Data to Insights end to end value chain.
- Strong hands-on expertise in Apache Spark, PySpark, Kafka, Flink, Airflow, NiFi, Trino, SQL, Python, Scala, and Java.
- Proven experience in designing and implementing open-source data platform architectures, including component selection, integration, scalability, interoperability, and platform modernization.
- Deep understanding of batch, streaming, and real-time data processing architectures, including performance tuning, workload optimization, and failure recovery.
- Experience building modern Data Lakehouse architectures using Apache Iceberg, Hudi, Delta Lake, object storage, and open table formats.
- Strong knowledge of event-driven architectures, CDC, APIs, schema management, and enterprise integration patterns.
- Expertise in DataOps practices, including Airflow orchestration, CI/CD, automated testing, observability, metadata management, lineage, and platform operations.
- Experience with PostgreSQL, Cassandra, MongoDB, OpenSearch, and enterprise analytics platforms, including data modeling, indexing, workload management, and availability design.
- Strong understanding of security, governance, and compliance, including RBAC, identity management, encryption, privacy controls, auditability, metadata governance, and vulnerability management.
- Proven ability to lead architecture definition, HLD/LLD creation, NFR assessment, migration planning, technical governance, and production readiness activities.
- Experience supporting large-scale data platform modernization, cloud migration, solutioning, RFP responses, effort estimation, and client-facing architecture discussions.
- Good understanding of BFSI regulatory, security, resilience, privacy, and operational requirements.
- Bachelor’s degree in computer science, Engineering, Information Systems, Data Management, or a related discipline.
- Preferred certifications in Cloud Data Engineering, Kubernetes, Enterprise Architecture, Data Management, Security, DevOps, or Open-Source Data Platforms.
- Experience assessing business use cases and selecting the appropriate approach across BI/KPI reporting, rules, statistical methods, forecasting, machine learning, and GenAI, with clear baselines, acceptance criteria, and measurable business KPIs.
- Understanding of AI/ML platform architecture supporting feature engineering, model training, batch and real-time inference, and integration of ML and GenAI workloads with enterprise open-source data platforms.
- Understanding of MLOps and LLMOps capabilities, including experiment tracking, model registry, feature stores, pipeline orchestration, automated quality gates, and versioning of data, models, and prompts using tools such as MLflow and Kubeflow.
- Exposure to AI observability and monitoring for data drift, model drift, LLM quality, tracing, latency, availability, alerting, and operational runbooks.
Roles and Responsibilities:
- Engage with business and technology stakeholders to understand data strategy, platform requirements, architecture goals, and transformation priorities.
- Design and define end-to-end architecture for enterprise open-source data platforms supporting batch, streaming, analytics, and AI workloads.
- Lead modernization initiatives covering data lakes, lake house 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 technologies such as Spark, Kafka, Flink, NiFi, and Trino.
- Establish data governance, security, metadata, lineage, privacy, and observability standards across the platform landscape.
- Evaluate and recommend open-source technologies based on business requirements, scalability, supportability, interoperability, 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, data engineering, platform implementation, migration, testing, deployment, and production readiness activities.
- Drive platform performance optimization, capacity planning, reliability engineering, and operational excellence initiatives.
- Conduct architecture reviews, identify risks early, and guide delivery teams on mitigation strategies and best practices.
- Support solutioning, RFP responses, effort estimation, technical presentations, and client workshops for strategic opportunities.
- Collaborate with engineering teams, platform vendors, open-source communities, and client stakeholders to deliver scalable and sustainable solutions.
- Mentor architects and engineers, contribute to technical capability development, and establish reusable accelerators, playbooks, and best practices.
- Support hiring, technical assessments, and knowledge-sharing initiatives across Data Engineering, Open-Source Platforms, and Modern Data Architecture.
- Assess candidate use cases with business, technology, data, and risk stakeholders; recommend BI, rules, analytics, ML, or GenAI based on feasibility, risk, cost, and measurable business outcomes.
- Integrate data, ML, and GenAI reference architectures covering data sources, feature pipelines, models or LLMs, vector stores, serving layers, consuming applications, and enterprise integration points.
- Partner with platform and operations teams to productionize AI ML and LLM workloads on Kubernetes.
Tata Consultancy Services Canada Inc. is committed to meeting the accessibility needs of all individuals in accordance with the Accessibility for Ontarians with Disabilities Act (AODA) and the Ontario Human Rights Code (OHRC). Should you require accommodation during the recruitment and selection process, please inform Human Resources.
Thank you for your interest in TCS. Candidates that meet the qualifications for this position will be contacted within a 2-week period. We invite you to continue to apply for other opportunities that match your profile.
