Lead Data Engineer

Quantiphi is an award-winning, AI-First global digital engineering company that helps the world’s leading Fortune 1000 organizations transform bold ideas into measurable business impact. We go beyond building innovative AI technologies—we solve the problems that matter most to our clients.

Since our founding in 2013, Quantiphi has built a proven track record of turning complex challenges into meaningful outcomes across industries.

Headquartered in Boston, with more than 4,000 professionals worldwide, we partner with global enterprises to deliver large-scale digital, cloud, and AI-driven transformation. #Solving What Matters

We are an Elite and Premier partner to Google Cloud, AWS, NVIDIA, Snowflake, and other leading technology platforms, and our work has been recognized across the industry, including:

• 21 Google Cloud Partner of the Year awards in the past 10 years
• 3 AWS AI/ML Partner of the Year awards
• 3 NVIDIA Partner of the Year awards
• 3 Snowflake Partner of the Year awards
• Rated Leaders by Gartner, Forrester, IDC, ISG, Everest Group and other leading analyst firms

Quantiphi delivers First-in-class AI solutions across Life Sciences, Healthcare, Banking, Financial Services, CPG, Manufacturing, Energy, High-Tech, Telecommunications, etc., powered by cutting-edge Generative AI and Agentic AI accelerators.

We are also proud to be certified as a Great Place to Work
—reflecting our commitment to our people and our culture.

Experience Level - 8 to 12 Years

The focus of a Lead Data Engineer is to manage and lead Data Engineers and to support and enhance all parts of the Enterprise Data Platform. The role also involves working closely with the rest of the delivery team which comprises Business Analysts, Testers, Delivery Managers and the business stakeholders. The right candidate will be able to:

• Working with senior stakeholders to understand high value business problems that can be solved through the application of data processing and analytical systems
• Demonstrate experience of Leading a team of data engineers, both onshore and offshore
• Capability to architect highly scalable distributed systems, using different tools
• Helping to architect, setup and support a new cloud-based analytics platform for the business
• Ensuring your team supports the Data Science Team by building data pipelines for specific insights and provides them with an environment for their R&D and experimentation
• Have experience of working with partners and business stakeholders
• Expert knowledge of data modelling and understanding of different data structures and their benefits and limitations under particular use cases
• Have Technical experience of Cloud Data platforms GCP
• Have technical experience of Microsoft data warehouse developer (SQL Server, SSIS, SSAS) and cloud based data warehousing tools such as Azure Synapse and Data Factory.
• Capable of solving and coaching the team through complex data problems
• Must be able to work both independently and as a member of a team to deliver enterprise class data warehouse solutions.
• Communication and presentation of ideas to colleagues in all parts of the wider business
• Recruitment, Mentoring & code reviews for the Data Engineering Team

We are looking for a skilled, motivated and analytical individual with a strong leadership skills and background in data warehousing who can work closely with the existing team to successfully deliver the requirements set out in the portfolio roadmap.

• Development background with exposure to Data Warehousing at scale on both on premise and cloud
• Experience developing ETL solutions in Cloud Data platforms GCP as well as SQL Server including SSIS & T-SQL
• Exposure to DW development practices (code & configuration management, build processes)
• Appreciation of design patterns and practices that apply to the data warehouse space (e.g. Data Modelling)
• Ability to take business requirements and work with other team members to estimate and design end-to-end solutions
• Good understanding of different types of data (i.e. structured, semi-structured and unstructured data) and the considerations that should be made in how they are stored.
• Solid understanding on how to…