About the role
Sequencr AI is on a mission to reshape how modern marketing and communications teams operate through intelligent, AI-driven solutions. We are a pre-seed, bootstrapped startup building solutions that delivers real business impact.
We’re seeking a Data Engineer to help design, build, and maintain the data infrastructure that powers our AI capabilities. You’ll work closely with our engineering and product teams to ensure our data systems are reliable, scalable, and optimized for growth. This role is ideal for someone who thrives in early-stage environments, enjoys solving complex data challenges, and wants to play a meaningful role in shaping an emerging AI platform.
Key Responsibilities
Design, build and maintain scalable, secure and high-performance data pipelines that support AI-driven content generation, marketing analytics, customer engagement and other use cases. Design and develop APIs, using frameworks such as FastAPI, to expose data from relational, graph and vector databases, as well as cloud storage, to real-time applications. Enable low-latency inference and feature retrieval for downstream systems. Build and maintain backend services that orchestrate ETL and ELT workflows, trigger pipelines, manage data-processing jobs and serve real-time data. Integrate data workflows with orchestration tools such as Prefect and cloud services on AWS. Develop adapters to search, retrieve and parse data from third-party platforms and APIs, including services such as SerpAPI. Build and maintain data crawlers, connectors and ingestion pipelines capable of processing large volumes of structured and unstructured external data. Optimize data workflows to support real-time insights and improve AI model performance. Develop and maintain data-driven decision-support systems that help clients improve content strategy, stakeholder analysis and customer engagement. Deploy, manage and optimize cloud-based data infrastructure, preferably on AWS, to support large-scale AI applications and high-throughput processing. Optimize data storage, indexing, retrieval and processing performance to improve AI-driven workflows and automation systems. Implement best practices for data security, governance, access control and compliance across data operations. Work closely with AI specialists, software engineers and product managers to align data architecture with business requirements and AI use cases. Define and enforce data-governance policies and best practices for handling customer data, content assets and analytics pipelines. Support AI model training, fine-tuning and deployment by structuring and optimizing datasets, embeddings and retrieval systems.
Qualifications and Skills
Experience with Neo4j and Cypher queries. Experience using Playwright for browser automation or application testing. Experience designing data systems for generative AI agents, semantic search, knowledge graphs or real-time intelligence applications. Experience working in an early-stage startup or similarly fast-moving product environment. At least 10 years of experience in data engineering, data science or AI and machine-learning infrastructure. Strong programming skills in Python and SQL, with experience using distributed-computing frameworks. Demonstrated experience designing, building and maintaining production-grade data infrastructure and backend services. Hands-on experience building ETL and ELT pipelines and real-time data-streaming systems using tools such as Kafka, Apache Airflow, Prefect or similar platforms. Experience building data crawlers, adapters, connectors and ingestion pipelines that retrieve and process large volumes of data from external websites, APIs and third-party platforms. Proficiency with cloud platforms, preferably AWS, including serverless processing, large-scale data storage and workflow orchestration. Strong knowledge of relational, NoSQL, graph and vector databases. Experience working with vector databases and building or orchestrating retrieval-augmented generation pipelines. Familiarity with enterprise data security, governance, compliance, encryption and access-control best practices. Strong communication skills and an ability to work effectively with both technical and non-technical colleagues. Experience working with AI-driven applications, natural-language-processing workflows and machine-learning pipelines is a strong asset.
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About Sequencr AI
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About the role
Sequencr AI is on a mission to reshape how modern marketing and communications teams operate through intelligent, AI-driven solutions. We are a pre-seed, bootstrapped startup building solutions that delivers real business impact.
We’re seeking a Data Engineer to help design, build, and maintain the data infrastructure that powers our AI capabilities. You’ll work closely with our engineering and product teams to ensure our data systems are reliable, scalable, and optimized for growth. This role is ideal for someone who thrives in early-stage environments, enjoys solving complex data challenges, and wants to play a meaningful role in shaping an emerging AI platform.
Key Responsibilities
Design, build and maintain scalable, secure and high-performance data pipelines that support AI-driven content generation, marketing analytics, customer engagement and other use cases. Design and develop APIs, using frameworks such as FastAPI, to expose data from relational, graph and vector databases, as well as cloud storage, to real-time applications. Enable low-latency inference and feature retrieval for downstream systems. Build and maintain backend services that orchestrate ETL and ELT workflows, trigger pipelines, manage data-processing jobs and serve real-time data. Integrate data workflows with orchestration tools such as Prefect and cloud services on AWS. Develop adapters to search, retrieve and parse data from third-party platforms and APIs, including services such as SerpAPI. Build and maintain data crawlers, connectors and ingestion pipelines capable of processing large volumes of structured and unstructured external data. Optimize data workflows to support real-time insights and improve AI model performance. Develop and maintain data-driven decision-support systems that help clients improve content strategy, stakeholder analysis and customer engagement. Deploy, manage and optimize cloud-based data infrastructure, preferably on AWS, to support large-scale AI applications and high-throughput processing. Optimize data storage, indexing, retrieval and processing performance to improve AI-driven workflows and automation systems. Implement best practices for data security, governance, access control and compliance across data operations. Work closely with AI specialists, software engineers and product managers to align data architecture with business requirements and AI use cases. Define and enforce data-governance policies and best practices for handling customer data, content assets and analytics pipelines. Support AI model training, fine-tuning and deployment by structuring and optimizing datasets, embeddings and retrieval systems.
Qualifications and Skills
Experience with Neo4j and Cypher queries. Experience using Playwright for browser automation or application testing. Experience designing data systems for generative AI agents, semantic search, knowledge graphs or real-time intelligence applications. Experience working in an early-stage startup or similarly fast-moving product environment. At least 10 years of experience in data engineering, data science or AI and machine-learning infrastructure. Strong programming skills in Python and SQL, with experience using distributed-computing frameworks. Demonstrated experience designing, building and maintaining production-grade data infrastructure and backend services. Hands-on experience building ETL and ELT pipelines and real-time data-streaming systems using tools such as Kafka, Apache Airflow, Prefect or similar platforms. Experience building data crawlers, adapters, connectors and ingestion pipelines that retrieve and process large volumes of data from external websites, APIs and third-party platforms. Proficiency with cloud platforms, preferably AWS, including serverless processing, large-scale data storage and workflow orchestration. Strong knowledge of relational, NoSQL, graph and vector databases. Experience working with vector databases and building or orchestrating retrieval-augmented generation pipelines. Familiarity with enterprise data security, governance, compliance, encryption and access-control best practices. Strong communication skills and an ability to work effectively with both technical and non-technical colleagues. Experience working with AI-driven applications, natural-language-processing workflows and machine-learning pipelines is a strong asset.
Not the right fit? Search for Data Engineer jobs in Canada