ShyftLabs

<div><strong>Position Overview:</strong></div>
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<div>We are seeking a Data Engineer to join a client-focused engagement, with responsibilities split evenly between production support and technical/development work. This role requires 2–4 years of hands-on experience in data engineering, strong proficiency in Python, SQL, and Spark, and prior exposure to client-based project environments. The ideal candidate will be comfortable balancing operational support duties with building and optimizing data pipelines.</div>
<h2>Job Responsibilities:</h2><ul><li>Provide day-to-day support (50%) for existing data pipelines, jobs, and platforms —monitoring, troubleshooting, and resolving issues to ensure smooth operations</li>
<li>Design, build, and maintain (50%) scalable data pipelines and ETL/ELT workflows using Python, SQL, and Spark</li>
<li>Collaborate with cross-functional and client teams to understand data requirements and translate them into technical solutions</li>
<li>Perform root-cause analysis on data/pipeline issues and implement fixes with minimal downtime</li>
<li>Optimize existing data workflows for performance, reliability, and cost-efficiency</li>
<li>Document processes, pipeline architecture, and support runbooks for knowledge continuity</li>
<li>Participate in on-call/support rotations as needed for the client engagement</li>
<li>Work with Databricks and/or AWS cloud environments where applicable to build or support data solutions</li></ul>
<h2>Basic Qualifications:</h2><ul><li>2–4 years of experience in a Data Engineering role</li>
<li>Strong proficiency in Python and SQL</li>
<li>Hands-on experience with Apache Spark</li>
<li>Prior experience working on client-based projects (mandatory)</li>
<li>Ability to work across both support and development responsibilities</li>
<li>Strong problem-solving and communication skills for client-facing situations</li></ul>
<h2>Preferred Skills:</h2><ul><li>Experience working with Databricks</li>
<li>Familiarity with AWS Cloud services (e.g., S3, Glue, EMR, Lambda, Redshift)</li>
<li>Exposure to CI/CD pipelines for data engineering workflows</li>
<li>Experience with workflow orchestration tools (e.g., Airflow)</li></ul>

Data Engineer

<h3><strong>Position Overview</strong></h3>
<div><br />We are looking for a Data Engineer with hands-on experience in building scalable data pipelines and data engineering solutions on the Databricks Lakehouse Platform. The ideal candidate should have strong expertise in Python, PySpark, SQL, Databricks, AWS, and REST API integrations for data ingestion, managing large volumes of data, and data export
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<div>ShyftLabs is a growing data product company that was founded in early 2020 and works primarily with Fortune 500 companies. We deliver digital solutions built to help accelerate the growth of businesses in various industries, by focusing on creating value through innovation.</div>
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<h2>Job Responsibilities:</h2><div>Design, develop, and maintain scalable ETL/ELT pipelines using Databricks,<br />PySpark, and SQL.<br />● Integrate data from multiple sources, including databases, Amazon S3, files, and REST APIs.<br />● Build data pipelines with Databricks Unity Catalog.<br />● Implement business logic, data transformations, and dimensional data models.<br />● Create, schedule, monitor, and optimize Databricks Jobs and Workflows.<br />● Design and manage Delta Lake tables using Medallion Architecture (Bronze, Silver,Gold).<br />● Ensure data quality through validations, error handling, logging, and monitoring.<br />● Optimize Spark workloads for performance, scalability, and reliability.<br />● Collaborate with cross-functional teams to deliver production-ready data solutions.</div>
<h2>Basic Qualification:</h2><div>Strong expertise in Python, PySpark, and Advanced SQL.<br />● Hands-on experience with the Databricks Lakehouse Platform.<br />● Good understanding of Unity Catalog, Delta Lake, Databricks Workflows/Jobs,<br />Clusters, Notebooks, Repos, and Medallion Architecture.<br />● Experience integrating with REST APIs for data ingestion and data export.<br />● Strong knowledge of ETL/ELT development, batch processing, incremental loading,<br />and data transformation.<br />● Experience with data modeling (Star Schema, Snowflake Schema, Fact &amp; Dimension<br />tables, SCD concepts).<br />● Understanding of data warehousing concepts and best practices.<br />● Experience working with structured and semi-structured data (CSV, JSON, Parquet,<br />Delta).<br />● Knowledge of partitioning, file optimization, Spark performance tuning, and query<br />optimization.<br />● Experience with Git and CI/CD best practices</div>
<h2>Preferred Qualifications:</h2><div>5+ years of experience in Data Engineering with 2+ years of hands-on Databricks<br />experience.<br />● Experience with Auto Loader, Spark Declarative pipelines, Kafka, Airflow, or dbt is a plus.<br />● Databricks certification is an added advantage.</div>

Technical Data Engineer (Databricks)

<div><strong>About ShyftLabs </strong></div>
<div>At ShyftLabs, we live and breathe data. Since 2020, we've been helping Fortune 500 companies unlock growth with cutting-edge digital solutions that transform industries and create measurable business impact. We're growing fast, and we're looking for passionate technical leaders who are excited to solve complex data challenges, build modern cloud platforms, and deliver innovative solutions for enterprise clients. </div>
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<div><strong>The Opportunity </strong></div>
<div>ShyftLabs is seeking an experienced Senior / Lead Data Engineer to lead the design, architecture, and delivery of enterprise-scale data platforms for Fortune 500 organizations. This is a highly client-facing leadership role responsible for owning projects from discovery through production deployment. You'll partner directly with client stakeholders to understand business objectives, define technical strategy, architect scalable cloud solutions, and lead engineering teams through successful delivery. The ideal candidate combines deep hands-on expertise with Databricks, Apache Spark, Python, SQL, and modern cloud platforms with proven experience leading complex data modernization initiatives. You'll play a key role in shaping technical direction, mentoring engineers, establishing engineering best practices, and delivering scalable data products that enable analytics, AI, and machine learning.</div>
<h2>What You'll Be Doing</h2><div><strong>Technical Leadership </strong></div>

<li>Lead the architecture, design, and implementation of enterprise-scale data platforms from project inception through production deployment.</li>
<li>Own technical delivery across multiple client engagements while ensuring high-quality engineering standards.</li>
<li>Define solution architecture, technical roadmaps, and implementation strategies aligned with client business goals. </li>
<li>Conduct architecture reviews, code reviews, and establish engineering best practices across project teams.</li>
<li>Mentor and coach Data Engineers while fostering technical excellence and continuous learning.</li>
<li>Serve as the primary technical leader for complex engineering initiatives and critical project decisions.</li>

<div><strong>Client Partnership </strong></div>

<li>Partner directly with Fortune 500 clients to understand business requirements and translate them into scalable technical solutions.</li>
<li>Lead discovery workshops, architecture sessions, and technical planning meetings with both business and engineering stakeholders.</li>
<li>Present solution designs, delivery plans, and architectural recommendations to technical leadership and executive audiences.</li>
<li>Build trusted relationships with client teams while providing technical guidance throughout project execution.</li>
<li>Support pre-sales activities by contributing technical expertise, solution estimates, and implementation approaches when required.</li>

<div><strong>Data Engineering &amp; Platform Development </strong></div>

<li>Design, develop, and optimize enterprise-grade data pipelines using the Databricks Unified Analytics Platform.</li>
<li>Build scalable ETL and ELT frameworks capable of processing large-scale structured and unstructured datasets.</li>
<li>Design and implement Lakehouse architectures using Delta Lake and Medallion design patterns.</li>
<li>Develop high-performance Spark applications for batch and real-time data processing.</li>
<li>Integrate data from enterprise applications, APIs, streaming platforms, and cloud storage solutions.</li>
<li>Ensure data quality, integrity, and reliability through automated validation, testing, and monitoring.</li>

<div><strong>Cloud &amp; DevOps </strong></div>

<li>Architect cloud-native data platforms across AWS, Azure, or Google Cloud Platform.</li>
<li>Implement Infrastructure-as-Code using Terraform or similar technologies.</li>
<li>Build and maintain CI/CD pipelines supporting automated testing and deployment.</li>
<li>Optimize cloud infrastructure for scalability, reliability, security, and cost efficiency.</li>
<li>Monitor platform performance and proactively resolve operational issues.</li>

<div><strong>Data Governance &amp; Security </strong></div>

<li>Implement enterprise data governance frameworks and security best practices.</li>
<li>Configure Unity Catalog, metadata management, lineage, and role-based access controls.</li>
<li>Ensure compliance with organizational security standards and regulatory requirements.</li>
<li>Promote data observability and operational excellence across production environments</li>

<div><strong>Cross-Functional Collaboration </strong></div>

<li>Partner closely with Product Managers, Data Scientists, Analytics Engineers, Machine Learning Engineers, and Software Engineers to deliver high-impact data products.</li>
<li>Enable AI and machine learning initiatives through scalable feature engineering pipelines and production-ready datasets.</li>
<li>Contribute reusable frameworks, accelerators, and engineering standards that improve delivery across client engagements.</li>
<h2>What You'll Bring</h2><ul><li>Bachelor's or Master's degree in Computer Science, Data Engineering, Software Engineering, or a related technical discipline.</li>
<li>8+ years of experience designing and building enterprise-scale data platforms.</li>
<li>5+ years of hands-on experience with Databricks and Apache Spark.</li>
<li>Proven experience leading enterprise data engineering projects from architecture through production delivery.</li>
<li>Strong expertise in Python, SQL, and Spark for large-scale data processing.</li>
<li>Deep understanding of Delta Lake, Lakehouse architecture, and modern data platform design.</li>
<li>Experience working with AWS, Azure, or Google Cloud Platform.</li>
<li>Strong knowledge of ETL/ELT frameworks, distributed computing, and data modeling.</li>
<li>Experience implementing CI/CD pipelines and Infrastructure-as-Code.</li>
<li>Strong understanding of data governance, security, metadata management, and data quality practices.</li>
<li>Experience optimizing distributed data processing workloads for performance and cost.</li>
<li>Excellent communication and stakeholder management skills with experience working directly with enterprise clients. </li>
<li>Demonstrated ability to mentor engineers and lead technical initiatives</li></ul>
<h2>Nice to Have</h2><ul><li>Databricks Certified Professional Data Engineer certification.</li>
<li>Experience with Delta Live Tables, MLflow, Unity Catalog, and Databricks SQL.</li>
<li>Experience with Kafka, Kinesis, Event Hubs, or other streaming technologies.</li>
<li>Hands-on experience with Snowflake, dbt, Airflow, or modern data orchestration tools.</li>
<li>Experience with Kubernetes, Docker, and Terraform. </li>
<li>Knowledge of AI/ML data platforms, Feature Stores, or Retrieval-Augmented Generation (RAG) architectures.</li>
<li>Previous consulting or professional services experience delivering solutions for enterprise clients.</li>
<li>Experience within retail, e-commerce, financial services, logistics, healthcare, or ad-tech environments.</li></ul>
<h2>Salary Range</h2><ul><li>$140,000 – $180,000 (CAD)</li></ul>

Senior Data Engineer

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<p><strong>About ShyftLabs</strong></p>
<p>At ShyftLabs, we live and breathe data. Since 2020, we’ve been helping Fortune 500 companies unlock growth through innovative digital solutions that drive real business impact. With teams across Canada, the U.S., and India, we’re growing quickly and looking for curious, motivated individuals who are excited to learn, build, and solve meaningful problems with technology.</p>
<p><strong>About Continuum</strong></p>
<p>Continuum is an enterprise AI agent execution and control platform designed to help teams build, run, and deploy reliable agentic applications. It provides agent orchestration, multi-model routing, persistent memory, tool integration, durable workflows, governance, guardrails, evaluation, and observability.</p>
<p>We are looking for an AI Engineer Intern interested in building production-ready AI agents and applications. You will contribute to the Continuum platform while also using it to build agentic applications for real enterprise use cases.</p>
<p>This is a hands-on engineering role. You will work across agent orchestration, memory systems, model optimization, retrieval, tools, guardrails, evaluation, and application development.</p>
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<h2>What You'll Be Doing</h2><div>

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<p>Build and improve agent orchestration and multi-agent workflows.</p>
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<p>Develop agentic applications for enterprise use cases using Continuum.</p>
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<p>Work with different commercial and open-source models, including OpenAI, Anthropic, Gemini, Llama, Qwen, Mistral, and others.</p>
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<p>Improve intelligent model routing based on task complexity, quality, latency, and cost.</p>
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<p>Build persistent memory and state-management capabilities for long-running agent workflows.</p>
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<p>Develop tool-calling functionality and integrations with APIs, databases, and enterprise systems.</p>
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<p>Work with MCP servers and function tools to connect agents with external services.</p>
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<p>Design context-engineering and retrieval pipelines using vector and graph databases.</p>
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<p>Implement AI guardrails for safety, security, access control, data privacy, and policy enforcement.</p>
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<p>Create evaluation pipelines to measure accuracy, groundedness, hallucination, tool usage, workflow completion, latency, and cost.</p>
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<p>Improve the observability and traceability of agent decisions, tool calls, and workflow execution.</p>
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<p>Optimize prompts, model usage, token consumption, response time, and infrastructure costs.</p>
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<p>Build APIs, backend services, and user-facing prototypes for agentic applications.</p>
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<p>Write clean, reusable, well-tested, and documented Python code.</p>
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<p>Contribute to Continuum’s open-source codebase, examples, documentation, and developer experience.</p>
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<h2>What We Are Looking For</h2><div>

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<p>Currently pursuing or recently completed a degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.</p>
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<p>Strong programming skills in Python.</p>
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<p>Good understanding of machine learning, natural language processing, and LLM fundamentals.</p>
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<p>Experience building at least one LLM-powered or agentic application.</p>
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<p>Familiarity with prompt engineering, embeddings, RAG, tool calling, and structured outputs.</p>
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<p>Experience working with APIs, Git, databases, and standard software engineering practices.</p>
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<p>Ability to research complex technical problems, experiment with different approaches, and clearly communicate results.</p>
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<p>Strong interest in building reliable AI systems, not just basic LLM demos</p>
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<h2>Nice to Have</h2><div>

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<p>Experience with agent frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, CrewAI, or similar technologies.</p>
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<p>Familiarity with OpenAI, Anthropic, Gemini, AWS Bedrock, or open-source models.</p>
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<p>Experience with vector databases such as Milvus, Pinecone, Weaviate, or Chroma.</p>
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<p>Experience with graph databases such as Neo4j.</p>
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<p>Knowledge of PostgreSQL, Redis, Docker, Kubernetes, or cloud infrastructure.</p>
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<p>Familiarity with AI observability and evaluation tools such as Langfuse.</p>
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<p>Understanding of multi-tenancy, identity management, authorization, or enterprise security.</p>
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<p>Experience with model routing, inference optimization, prompt compression, or cost optimization.</p>
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<p>Contributions to open-source AI projects, research, hackathons, or technically strong personal projects.</p>
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<h2>Hourly Pay</h2><ul><li>$20 - $30/Hr (CAD)</li></ul>

AI Engineer Intern

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<h2><span><strong>About ShyftLabs</strong></span></h2>
<p>ShyftLabs is a fast-growing data and AI company that helps organizations unlock the value of their data through modern engineering, analytics, and intelligent products. We partner with Fortune 500 organizations to build scalable cloud solutions, AI-driven platforms, and innovative data products that accelerate business outcomes.</p>
<p>We're looking for a <strong>Data Product Manager</strong> to join our growing Data Products team. This role is ideal for someone who combines strong product management expertise with a deep understanding of modern data platforms and enjoys partnering directly with clients to translate complex business challenges into scalable analytical solutions.</p>
<p>Reporting to the <strong>Director of Analytical Data Products</strong>, you'll own the strategy, roadmap, and execution for a portfolio of analytical data products while serving as the primary point of contact for client stakeholders. You'll collaborate closely with engineering, analytics, and architecture teams to deliver high-quality, reusable data products that drive measurable business value.</p>
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<h2>What You'll Do</h2><div>
<h3>Product Strategy &amp; Ownership</h3>
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<li>Own the end-to-end lifecycle of analytical data products, from discovery and roadmap planning through delivery, adoption, and continuous improvement.</li>
<li>Define product vision and priorities aligned with client business objectives and organizational strategy.</li>
<li>Balance new feature development with technical debt reduction, platform scalability, and long-term product sustainability.</li>
<li>Monitor product performance and usage metrics to identify opportunities for optimization and future enhancements.</li>

<h3>Client Partnership</h3>
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<li>Serve as the primary client-facing product lead throughout product delivery.</li>
<li>Lead discovery workshops, requirements gathering sessions, and solution scoping discussions.</li>
<li>Translate business objectives into clear product requirements and prioritized development backlogs.</li>
<li>Build trusted relationships with client stakeholders by communicating technical concepts in a clear, business-focused manner.</li>
<li>Manage stakeholder expectations while balancing competing priorities across multiple initiatives.</li>

<h3>Product Delivery</h3>
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<li>Maintain, prioritize, and refine product backlogs based on evolving business needs.</li>
<li>Partner closely with Engineering, Analytics, Architecture, and Data teams to deliver scalable analytical data products.</li>
<li>Lead Agile ceremonies including sprint planning, backlog refinement, stand-ups, retrospectives, and release planning.</li>
<li>Ensure successful product launches and ongoing operational excellence.</li>

<h3>Data &amp; Technical Leadership</h3>
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<li>Collaborate with engineering teams to define data quality, governance, testing, and usability standards.</li>
<li>Contribute to architectural discussions involving modern data platforms including Snowflake, BigQuery, ClickHouse, and similar technologies.</li>
<li>Advocate for reusable, scalable data products using modern architectural principles including Data Mesh, Data Fabric, Semantic Layer, and FAIR principles.</li>
<li>Perform hands-on SQL analysis and data validation when necessary to support product decisions and client discussions.</li>

<h3>Cross-Functional Collaboration</h3>
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<li>Partner with Legal, Privacy, Compliance, and Security teams to ensure products meet regulatory and governance requirements.</li>
<li>Work closely with executive stakeholders to communicate product strategy, delivery progress, and technical tradeoffs.</li>
<li>Foster a product-led culture focused on delivering measurable business value through data.</li>

</ul></ul></ul></ul></ul></div>
<h2>What You Bring</h2><div>

<li>Bachelor's degree in Computer Science, Data Science, Information Systems, Business, Engineering, or a related discipline.</li>
<li>3–5+ years of experience in Product Management, Data Product Management, Data Management, Analytics, or Data Science.</li>
<li>Proven experience managing the full product lifecycle from discovery through launch and continuous improvement.</li>
<li>Strong client-facing experience with the ability to gather requirements, influence stakeholders, and manage expectations.</li>
<li>Advanced SQL skills with the ability to analyze datasets and validate business hypotheses.</li>
<li>Experience working with modern cloud data platforms such as Snowflake, BigQuery, ClickHouse, Databricks, Redshift, or similar technologies.</li>
<li>Working knowledge of:
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<li>Data governance</li>
<li>Semantic layer concepts</li>
<li>FAIR principles</li>
<li>Modern data architecture patterns</li>


<li>Experience working within Agile product development environments.</li>
<li>Excellent communication and presentation skills with the ability to explain complex technical concepts to both technical and non-technical audiences.</li>

</ul></li></div>
<h2>Nice to Have</h2><div>

<li>Master's degree in Engineering, Data Science, Business, or a related field.</li>
<li>Experience within Pharma, Biotechnology, Financial Services, Consumer Packaged Goods (CPG), or Management Consulting.</li>
<li>Familiarity with Customer Data Platforms (CDPs), Omnichannel Marketing, or Digital Marketing Analytics.</li>
<li>Demonstrated experience using product usage metrics and customer insights to prioritize roadmap investments.</li>
<li>Experience working with enterprise-scale data products serving multiple business units or external clients.</li>

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<h2>Salary Range</h2><ul><li>$130,000 - $160,000 (CAD)</li></ul>

ShyftLabs

Open jobs at ShyftLabs

Data Engineer

Technical Data Engineer (Databricks)

Senior Data Engineer

AI Engineer Intern

Data Product Manager – Analytical Data Products

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