Lovelytics
Lovelytics is a team of elite data and AI professionals who know how to make complex problems feel manageableand even enjoyable. We bring clarity, speed, and heart to every project, partnering with clients to solve real problems with smart, sustainable solutions.
We are proud to be recognized as a top-tier Elite Partner in the Databricks Partner Program. Headquartered in Arlington, VA, we operate with a global delivery model across the United States, Canada, Colombia, Argentina, and Spain. Our international team is made up of more than 550 professionals worldwide.
Primary Responsibilties Formulate forward-looking data strategies aligned with client business objectives and industry best practices Design and oversee large-scale lakehouse and warehouse implementations on Databricks (must-have) and other cloud-native technologies Create solutions that integrate on-premises and multiple cloud environments seamlessly. Architect batch and streaming ingestion, real-time processing, and ELT/ETL patterns Ensure security, privacy, compliance, and data quality at scale on client engagements Tackle intricate data engineering challenges and make strategic decisions to de-risk delivery Introduce emerging technologies and methodologies to keep client solutions at the cutting edge. Drive performance, cost optimization, scalability, and maintainability across data engineering solutions Mentor engineers, review architectures and code, and guide teams through implementation. Lead technical discovery, shape solution architectures, respond to RFPs, and deliver demos and proofs of concept for data engineering engagements Create technical blueprints and recommend tools, frameworks, and design patterns aligned to client needs
Required Qualifications Bachelors or Masters degree in Computer Science, Engineering, or a related field. 8+ years of experience in data engineering and architecture, including large-scale cloud deployments 4+ years in a client facing role, preferably in a professional services firm Proven track record designing and implementing modern data lakehouses, warehouses, and pipelines in AWS, Azure, or GCP Expert knowledge of Databricks and Spark (required) Experience creating proofs of concept, technical presales presentations, and pricing for engagements Strong client-facing communication skills with the ability to influence technical and executive stakeholders A tech stack of Google Workspace (email, tools), MacOS, Slack (internal comms), Atlassian
We are proud to be recognized as a top-tier Elite Partner in the Databricks Partner Program. Headquartered in Arlington, VA, we operate with a global delivery model across the United States, Canada, Colombia, Argentina, and Spain. Our international team is made up of more than 550 professionals worldwide.
Primary Responsibilties Formulate forward-looking data strategies aligned with client business objectives and industry best practices Design and oversee large-scale lakehouse and warehouse implementations on Databricks (must-have) and other cloud-native technologies Create solutions that integrate on-premises and multiple cloud environments seamlessly. Architect batch and streaming ingestion, real-time processing, and ELT/ETL patterns Ensure security, privacy, compliance, and data quality at scale on client engagements Tackle intricate data engineering challenges and make strategic decisions to de-risk delivery Introduce emerging technologies and methodologies to keep client solutions at the cutting edge. Drive performance, cost optimization, scalability, and maintainability across data engineering solutions Mentor engineers, review architectures and code, and guide teams through implementation. Lead technical discovery, shape solution architectures, respond to RFPs, and deliver demos and proofs of concept for data engineering engagements Create technical blueprints and recommend tools, frameworks, and design patterns aligned to client needs
Required Qualifications Bachelors or Masters degree in Computer Science, Engineering, or a related field. 8+ years of experience in data engineering and architecture, including large-scale cloud deployments 4+ years in a client facing role, preferably in a professional services firm Proven track record designing and implementing modern data lakehouses, warehouses, and pipelines in AWS, Azure, or GCP Expert knowledge of Databricks and Spark (required) Experience creating proofs of concept, technical presales presentations, and pricing for engagements Strong client-facing communication skills with the ability to influence technical and executive stakeholders A tech stack of Google Workspace (email, tools), MacOS, Slack (internal comms), Atlassian