• Assess the current (“as-is”) data platform and define the future-state architecture in collaboration with the Big Data Engineering leadership • Perform gap analysis and contribute to the implementation of the future-state data platform • Work closely with internal stakeholders including team leads, enterprise architecture, data governance, and data DevOps teams • Draft and review big data architectures alongside senior engineering and BI leadership to ensure optimal technical solutions • Support refinement sessions by contributing accurate effort sizing and architectural input • Provide guidance to avoid silos by ensuring architectural decisions are inclusive and scalable across teams • Contribute to project planning through architectural insight and risk awareness • Promote best practices across the Big Data Engineering team, ensuring data accuracy, validity, reusability, and consistent coding standards • Stay up to date with emerging Big Data technologies and trends, communicating their value clearly to both technical and non-technical audiences • Support collective peer review practices and improve overall architectural quality

• Develop a Modern Azure Data Platform: Design, build, and operate end-to-end data solutions using Azure Data Factory, Azure Data Lake Storage Gen2, Databricks, and Azure Synapse Analytics. • Create Data Pipelines: Develop and maintain scalable ETL/ELT pipelines using PySpark and Spark, with a strong focus on data quality, reliability, and performance. • Multi-Tenant & Environment Support: Support and operate multi-tenant data platforms across multiple environments (development, test, production) with clear separation and governance. • Infrastructure & Platform Operations: Provision, configure, and maintain Azure data infrastructure, ensuring stability, security, and scalability. • CI/CD for Data Platforms: Build and maintain CI/CD pipelines for data pipelines and Databricks workloads, enabling automated deployments across environments. • Cost-Efficient & Best-Practice Azure Usage: Apply Azure best practices to optimize performance and cost, including resource sizing, lifecycle management, and cost monitoring. • Collaboration with BI & Data Teams: Work closely with BI and data teams to support efficient data models and reporting solutions. • Data Governance & Security Basics: Support data governance requirements such as access control, secure data handling, and basic metadata management.

Caylent is a cloud native services company that helps organizations bring the best out of their people and technology using Amazon Web Services (AWS). We provide a full-range of AWS services including workload migrations and modernization, cloud native application development, DevOps, data engineering, security and compliance, and everything in between. At Caylent, our people always come first. We are a global company and operate fully remote with employees in Canada, the United States, and Latin America. We celebrate the culture of each of our team members and foster a community of technological curiosity. Come talk to us to learn more about what it means to be a Caylien! About the Job We are seeking a Principal Data Strategist to join our strategic customer portfolio. This role embeds you with our most important enterprise clients—working side-by-side with their executives, data leaders, and business stakeholders to transform how they leverage data as a strategic asset. This is not a traditional data consultant role. As a Principal Data Strategist, you'll bridge business strategy and technical execution, owning the "why" and "what" of data transformation while partnering with architects on the "how." You'll have direct line of sight to your customers' biggest business challenges and will design the strategic roadmaps that unlock measurable value from their data investments. As a Principal Data Strategist, you'll be at the intersection of strategy, governance, and AI enablement, helping customers build modern data platforms that drive business outcomes. You'll have the autonomy to shape strategic direction, the backing of a leading AWS Premier Partner, and the opportunity to define how data strategy is delivered at scale. Your Assignment - Lead Strategic Planning Engagements: Own the full lifecycle of outcome-focused planning engagements from discovery through executive readout, helping organizations systematically leverage data as a strategic asset. - Diagnose and Align Stakeholders: Facilitate alignment across executive sponsors and business consumers using strategy frameworks, defining measurable business outcomes that connect to real impact. - Build Executive-Ready Business Cases: Develop TCO/ROI models and financial justifications that articulate data equity and platform investments in language that resonates with the C-suite. - Design Data Governance Frameworks: Create operating models, stewardship structures, policies, and compliance controls for both structured and unstructured data environments. - Deliver Implementation Roadmaps: Build actionable, phased roadmaps with clear accountability, organizational change management strategies, and realistic milestones. - Assess Data and AI Maturity: Conduct baselining across strategy, processes, architecture, governance, and people dimensions, including AI/GenAI readiness for RAG and analytics agents. - Drive Strategic Account Growth: Uncover new opportunities through planning engagements and collaborate with Account Executives and Customer Solutions Architects to develop the pipeline. - Support Talent and Teams: Perform technical evaluations for strategist and architect candidates, and provide strategic guidance to engagement teams. - Advance the Practice: Refine methodologies, create reusable frameworks, and codify lessons learned to mature Caylent's Modern Data Strategy offering. Requirements Experience - 4+ years in the AWS data and analytics landscape - 10+ years in data strategy, governance, architecture, or analytics consulting with enterprise clients Skills & Capabilities: - Proven ability to align data strategy with business outcomes and communicate the "why" as clearly as the "how" - Quick learner who can rapidly understand a client's data landscape, workflows, organizational dynamics, and constraints - Strong consulting mindset: able to diagnose, scope, and structure solutions to ambiguous data challenges - Exceptional communicator - able to shift seamlessly between technical discussions with data architects and strategic conversations with C-suite executives - Expert facilitator of workshops and strategy sessions with excellent storytelling skills - Deep understanding of modern data platforms (Databricks, Snowflake, AWS-native stack) - Experience with AI/ML strategy and hands-on use of enterprise AI platforms (Amazon Bedrock, SageMaker, or comparable) Domain Expertise: - Data governance frameworks, data quality management, and regulatory compliance (GDPR, CCPA, HIPAA, or similar) - Strong drive to standardize methodologies and create reusable frameworks Flexibility: - Willingness to travel for customer and AWS meetings Preferred Qualifications - AWS certifications or data governance certifications - Experience working across regulated industries (financial services, insurance, healthcare, higher education) - Background partnering with data architects and engineers to translate strategy into technical execution - Experience building or scaling a data strategy practice within a consulting organization Benefits - Medical Insurance for you and eligible dependents - 401k plan with company match up to 4% and immediate vesting - Competitive phantom equity - Company issued laptop - Dental and Vision insurance - Term Disability Insurance - Term Life Insurance - Flexible Spending Account - Equipment & Office Stipend - Annual stipend for Learning and Development - Unlimited Paid Time Off - 10 Paid Holidays This role may require up to 25% travel, depending on business needs. Base Salary Range: The expected base salary range for this position is $185,000 - $210,000 per year, commensurate with experience and qualifications. Additional Compensation Components: In addition to the base salary, the compensation package may include bonuses, commissions, equity, and other incentives. The specific components will vary depending on the role and individual and/or company performance. NOTE: We’re unable to provide visa sponsorship now or at any time in the future. At Caylent, we are committed to fair, transparent, and inclusive hiring practices. As part of our recruitment process, we may use artificial intelligence (AI) tools or automated systems to assist with the screening and evaluation of applications to help match candidate qualifications with job requirements. These tools are designed to support — not replace — human decision-making. Final hiring decisions are always made by our trained recruitment professionals. If an AI or automated tool is used during your application process, it will only be in accordance with applicable laws and regulations, and your information will be handled in a secure and confidential manner. If you have any questions, please contact talent@caylent.com Caylent is a place where everyone belongs. We celebrate diversity and are committed to creating an inclusive environment for all employees. Our approach helps us to build a winning team that represents a variety of backgrounds, perspectives, and abilities. So, regardless of how your diversity expresses itself, you can find a home here at Caylent. We are proud to be an equal opportunity employer. We prohibit discrimination and harassment of any kind based on race, color, religion, national origin, sex (including pregnancy), sexual orientation, gender identity, gender expression, age, veteran status, genetic information, disability, or other applicable legally protected characteristics. If you would like to request an accommodation due to a disability, please contact us at hr@caylent.com.

• Own end-to-end reliability for core data pipelines—from ingestion through billing and reporting outputs. • Build observability foundations: health dashboards, tiered alerting and on-call signals that are actionable, not noisy. • Harden data ingestion with typed staging, quarantine patterns, freshness gates and safe backfill and replay capabilities. • Enforce schema contracts and quality validation so failures are caught in CI, not discovered by stakeholders. • Establish clear boundaries between governed core data and downstream BI and reporting layers. • Lead incident retrospectives that fix root causes, not just symptoms, and reduce future on-call burden. • Mentor 2–4 data engineers through code reviews, pairing and growth conversations.