Architect, Data Engineering

Accountabilities: Develop forward-looking data strategies that align client business objectives with industry best practices.

Design and oversee large-scale lakehouse and data warehouse implementations using Databricks and cloud-native technologies.

Architect solutions that seamlessly integrate on-premises systems with multiple cloud environments.

Design batch and streaming ingestion, real-time processing, and ELT/ETL architectures.

Establish solutions that support security, privacy, compliance, and data quality at scale.

Solve complex data engineering challenges and make strategic technical decisions that reduce delivery risks.

Evaluate and introduce emerging technologies, methodologies, and engineering practices to continuously improve client solutions.

Drive performance, cost optimization, scalability, reliability, and maintainability across data platforms.

Mentor engineers, review architectures and code, and provide technical guidance throughout implementation.

Lead technical discovery sessions and shape solution architectures based on client requirements.

Support presales activities by responding to RFPs, developing technical proposals, delivering demonstrations, and building proofs of concept.

Create technical blueprints and recommend appropriate tools, frameworks, and design patterns for client environments.

Partner with sales and account teams to scope engagements and communicate technical solutions effectively.

Requirements:

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.

5+ years of experience in data engineering and architecture, including experience with large-scale cloud deployments.

4+ years of experience in client-facing roles, preferably within a professional services or consulting environment.

Proven experience designing and implementing modern data lakehouses, warehouses, and data pipelines across AWS, Azure, or GCP.

Expert-level knowledge of Databricks and Apache Spark.

Experience developing proofs of concept, technical presales presentations, and engagement pricing.

Strong understanding of modern data engineering architecture, cloud platforms, data integration, and processing patterns.

Excellent client-facing communication skills, with the ability to influence both technical and executive stakeholders.

Strong problem-solving and decision-making skills, particularly when working through complex or ambiguous technical challenges.

Ability to mentor engineers and provide clear technical direction across delivery teams.

Comfortable working across technical delivery, architecture, consulting, and presales responsibilities.

Familiarity with Google Workspace, macOS, Slack, and Atlassian tools.

Strong ability to translate business requirements into scalable, maintainable technical solutions.

Benefits:

Base salary range of $155,000–$215,000 USD per year, depending on experience, skills, qualifications, role scope, and budget.

Remote U.S. roles offer a salary range of $170,000–$190,000 USD.

Remote work opportunities across the United States.

Opportunity to work on large-scale data and AI modernization initiatives for enterprise clients.

Exposure to Databricks, AWS, Azure, GCP, and modern cloud-native data technologies.

Opportunity to work across diverse industries and complex technical environments.

Significant ownership over data architecture, technical strategy, and solution delivery.

Collaborative environment with opportunities to mentor engineers and influence technical practices.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether? 

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