Integration Engineer Jobs

About The Position

Requirements

• 8-10 years of experience in Data Engineering/ support, or related analytics disciplines, preferably within large enterprise environments.
• 3+ years experience as a Technical Delivery leader
• Demonstrated experience in leading technical delivery for enterprise data integration, data warehousing, ETL/ELT processes, cloud data platforms, data governance, and production analytics or AI platforms.
• Strong proficiency in SQL, Python, or similar, and experience working with large-scale datasets across cloud and on-premises environments.
• Experience using analytical and visualization tools such as Power BI, Tableau, Python, Azure Data Factory, Azure AI Search, or equivalent technologies.
• Strong communication and stakeholder management skills, with the ability to influence decisions across business and technology organizations.
• Demonstrated ability to lead initiatives, manage priorities, and deliver results in fast-paced, highly regulated environments.
• Familiarity with MLOps, CI/CD, and cloud-based AI infrastructure.
• Knowledge of Agile delivery methodologies and experience working working within cross-functional product teams.
• Bachelor’s degree in computer science, Information Systems, Data Analytics, Statistics, Engineering, Mathematics, Business Analytics, or a related quantitative field.

Nice To Haves

• Experience working within financial services, banking, risk, compliance, or other highly regulated industries is a plus.
• Knowledge of Responsible AI, Model Risk Management, SR 11-7, OSFI E-23, or related governance frameworks is a plus.
• Master’s degree in data science, Analytics, Computer Science, Information Management, Business Administration, or a related field is preferred.
• Relevant certifications in Data Management, Cloud Platforms (Azure/AWS), Analytics, AI, or Data Governance are considered an asset.

Responsibilities

• Deep hands-on technical leadership with enterprise data onboarding, ingestion, and integration initiatives supporting AI, analytics, and business intelligence use cases.
• Lead and partner with Product Owners, Data Engineers, Data Scientists, and business stakeholders to translate business needs into actionable data requirements, data models, and integration strategies.
• Design and implement reliable, scalable data ingestion and integration pipelines for structured, semi-structured, unstructured data (e.g., databases, files, documents, APIs, events), and multi-modal data, ensuring data is AI ready, governed, secure, and observable.
• Ensure pipelines follow enterprise governance, access control, and security standards, including role-based access and lineage considerations. Monitor pipeline performance, troubleshoot failures, and optimize cost and throughput.
• Document processes, share knowledge, and contribute to a culture of continuous learning and responsible innovation.
• Establish and monitor data quality standards, controls, and metrics to ensure accuracy, completeness, timeliness, and consistency.
• Partner with Data Governance, Risk, Compliance, and Model Risk Management teams to ensure adherence to enterprise data policies, regulatory requirements, and Responsible AI standards.
• Support data lineage, metadata management, data cataloging, and traceability capabilities across ingestion and integration platforms.
• Collaborate with AI and Data Science teams to prepare, validate, and optimize datasets for machine learning, Generative AI, and advanced analytics applications.
• Support multimodal data ingestion initiatives involving documents, images, audio, video, and enterprise knowledge repositories.
• Analyze performance and effectiveness of chunking, indexing, retrieval, and data preparation strategies used in RAG and AI Search solutions.
• Develop production‑grade services and AI capabilities using Python, REST APIS, JSON/XML, vector databases, RAG evaluation and retrieval metrics.
• Develop analytical frameworks and KPIs to measure data platform effectiveness, ingestion performance, quality trends, and business outcomes.
• Evaluate emerging AI and data management technologies and recommend opportunities to improve DII capabilities.
• Lead cross-functional initiatives from discovery through production, partnering with business, technology, architecture, risk, security, and operations teams.
• Provide mentorship and guidance to analysts and junior team members, fostering a culture of continuous learning and analytical excellence and technical accountability.
• Communicate complex technical findings, risks, and recommendations to both technical and non-technical audiences, including senior leadership.
• Own and drive continuous improvement initiatives that increase automation, operational efficiency, and scalability of data ingestion and integration processes.

Benefits

• health insurance
• tuition reimbursement
• accident and life insurance
• retirement savings plans