Data Solution Architect designing scalable Databricks lakehouses and pipelines. Guiding cloud data engineering delivery for Lovelytics’ enterprise consulting clients.
Responsibilities
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 and other cloud-native technologies
Create solutions integrating on-premises and multiple cloud environments
Architect batch and streaming ingestion, real-time processing, and ELT/ETL patterns
Ensure security, privacy, compliance, and data quality at scale
Tackle intricate data engineering challenges and make strategic decisions to de-risk delivery
Introduce emerging technologies and methodologies
Drive performance, cost optimization, scalability, and maintainability
Mentor engineers, review architectures and code, and guide implementation teams
Lead technical discovery, shape solution architectures, respond to RFPs, and deliver demos and proofs of concept
Create technical blueprints and recommend tools, frameworks, and design patterns
Partner with sales and account teams to scope engagements and shape technical proposals
Requirements
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
8+ years of experience in data engineering, analytics architecture, governance and cloud, including large-scale cloud deployments
4+ years in a client-facing role in a professional services firm, scoping, creating architecture, and taking part in presales
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
Experience with Google Workspace, MacOS, Slack, and Atlassian
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