This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI Data Platform Engineer based in the United States.
This is a senior, architecture-focused opportunity to shape the foundation of a modern enterprise data ecosystem. You will define scalable architectures spanning ingestion, storage, processing, governance, and data consumption. The role combines hands-on technical depth with strategic decision-making across complex data environments. You’ll work closely with data engineering, analytics, ML, product, and business stakeholders to turn platform capabilities into measurable value. You will establish standards for data modeling, security, reliability, governance, and lifecycle management. The position offers the opportunity to influence major platform initiatives while mentoring engineers and guiding architectural direction. This is a fully remote role designed for an experienced data professional comfortable leading cross-functional programs.
Accountabilities
- Define the target-state architecture for an enterprise data platform across ingestion, storage, processing, governance, and consumption layers.
- Establish technical standards for data modeling, schema evolution, partitioning, file formats, storage organization, and data lifecycle management.
- Architect modern lakehouse, warehouse, and streaming solutions using technologies such as Snowflake, Databricks, BigQuery, Redshift, Iceberg, Delta Lake, or Hudi.
- Design end-to-end batch and streaming data pipelines that balance performance, latency, reliability, cost, and maintainability.
- Lead the integration of data governance, lineage, cataloging, and discovery capabilities using platforms such as Collibra, Alation, Atlan, Unity Catalog, or DataHub.
- Define security architectures covering identity-aware access, encryption, masking, and row- and column-level controls.
- Partner with ML, BI, product, analytics, and business teams to ensure the platform meets downstream data consumption requirements.
- Establish data contract and data product principles that promote clear ownership, quality, scalability, and effective separation between producers and consumers.
- Lead architecture reviews, evaluate proposed designs, and provide technical guidance to engineering and architecture teams.
- Drive data-platform cost optimization, capacity planning, high availability, disaster recovery, and multi-region strategies for critical assets.
- Mentor data engineers and architects while promoting modern platform standards, architectural consistency, and emerging best practices.
- Produce architecture documentation, including context diagrams, architecture decision records, reference architectures, and reusable design patterns.
- Monitor developments across data-platform technologies, vendors, research, and open-source ecosystems to inform future architectural decisions.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related discipline.
- 8+ years of experience in data engineering, including substantial experience in data architecture or platform architecture roles.
- Deep expertise with at least two major data platforms, such as Snowflake, Databricks, BigQuery, or Redshift.
- Strong understanding of modern lakehouse architectures, table formats, distributed data processing, and streaming systems.
- Production-scale experience with technologies such as Spark, Flink, or Kafka.
- Strong data modeling capabilities across dimensional, normalized, and data-vault approaches.
- Demonstrated experience implementing data governance, lineage, cataloging, quality, and ownership frameworks.
- Solid knowledge of cloud platforms, networking, identity and access management, security, and data-platform cost optimization.
- Proven track record of leading complex, cross-functional data-platform initiatives from architecture through implementation.
- Strong communication, facilitation, presentation, and stakeholder-management skills, with the ability to translate complex technical concepts for both technical and business audiences.
- Experience with data mesh or data product architectures is preferred.
- Familiarity with semantic-layer technologies such as dbt Semantic Layer, Cube, or LookML is advantageous.
- Experience working in regulated environments involving data residency, compliance, or audit requirements is a plus.
- Cloud or platform certifications across Snowflake, Databricks, AWS, Azure, or GCP are advantageous.
- Public speaking, technical writing, or contributions to the data architecture community are a plus.
Benefits
- Competitive salary: $135,000–$170,000 annually.
- Remote flexibility: 100% remote position within the United States.
- Employment type: Full-time, direct W-2 position.
- Career growth: Opportunity to shape enterprise-scale data architecture and influence major technology initiatives.
- Technical impact: Work across modern cloud data platforms, lakehouse architectures, streaming technologies, governance, and data products.
- Leadership opportunities: Mentor engineers and architects while establishing technical standards and architectural best practices.
- Collaborative environment: Partner with engineering, analytics, ML, product, and business stakeholders across the organization.
- Innovation-focused work: Stay at the forefront of evolving data-platform technologies, cloud services, and open-source developments.
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!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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