Principal Analytical Engineer

About Shyft Labs

At Shyft Labs, 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 problem‑solvers who are ready to turn big ideas into real outcomes.

We’re looking for a Principal Analytics Engineer to lead the design and evolution of enterprise‑grade data platforms and analytics solutions that power our clients’ next generation of digital and AI‑driven experiences. This role is for a senior technical leader who brings clarity to complexity, owns execution end‑to‑end, and sets the architectural direction for scalable, resilient data systems.

As a Principal Analytics Engineer, you’ll partner closely with clients, product leaders, and engineering teams to design and implement modern data architectures, ranging from data lakes and analytics platforms to AI and ML ready data layers. You’ll operate as both a hands‑on architect and a strategic advisor, shaping long‑term data strategy while remaining deeply involved in technical execution.

This role is ideal for someone with a strong data engineering or data architecture background
, extensive experience with enterprise‑level data systems
, and a proven ability to lead through influence in ambiguous, fast‑moving environments.

• Own the technical vision and architecture for analytics and data platforms, ensuring solutions are scalable, secure, and aligned with enterprise standards.
• Lead the design and implementation of end‑to‑end data architectures
  , including data lakes, data warehouses, analytics layers, and ML‑ready data pipelines.
• Define and evolve data modelling standards, analytics patterns, and architectural best practices across projects and teams.
• Navigate high levels of ambiguity by decomposing complex business and technical problems, proposing structured solution options, and driving alignment with stakeholders.
• Formulate, compare, and present multiple architectural and technical approaches, guiding clients and internal teams toward optimal long‑term solutions.
• Architect and build high‑quality, production‑grade data pipelines that support analytics, reporting, experimentation, and machine learning use cases at scale.
• Partner directly with clients to understand business objectives, translate them into robust technical designs, and act as a trusted technical advisor.
• Lead and mentor cross‑functional teams, including Analytics Engineers, Data Engineers, ML Engineers, and FE/BE developers, setting a high bar for technical quality.
• Influence and contribute to data governance, data quality, observability, and platform reliability initiatives.
• Drive the development of internal data products, reusable frameworks, accelerators, and AI‑powered solutions.
• Contribute to technical strategy, roadmap planning, and decision‑making across multiple engagements or accounts.

• 5+ years of extensive SQL and Python experience, with a strong ability to design, optimize, and troubleshoot complex data systems.
• 5+ years of relevant data engineering or data architecture experience, with the hands‑on ability to build and scale enterprise‑level data platforms.
• Proven experience designing and implementing data lakes, data warehouses, and modern analytics architectures
  .
• Demonstrated experience working on AI, ML, or advanced analytics initiatives
  , including preparing data for modeling and production use.
• Strong foundation in data modelling, distributed systems, and performance optimization.
• Experience working with major cloud platforms (
  AWS, GCP, or Azure
  ) in production, enterprise environments.
• Proven ability to operate independently with full ownership, while influencing technical direction across teams and stakeholders.
• Track record of successfully navigating ambiguity and driving outcomes in complex, client‑facing environments.
• Experience leading, mentoring, and influencing senior engineers and cross‑functional teams.
• Prior experience in a data, analytics, or ML‑focused organization or large‑scale enterprise project
  .

• Experience with modern data…