Data Engineer Level 3

The Data Engineer is a individual contributor (IC) responsible for designing, building, and evolving the companys data platform and high-impact data products so analytics, BI, AI/ML, and operational use cases are reliable, secure, and scalable. This role blends hands-on engineering with technical leadership: setting patterns and standards, driving cross-team alignment, and unblocking complex delivery across the data ecosystem.

Roles & Responsibilities:

• Shape and drive enterprise-wide data architecture strategy: Define and evolve the long-term technical vision for scalable, resilient data infrastructure across multiple business units and domains.
• Lead large-scale, cross-functional initiatives: Architect and guide the implementation of data platforms and pipelines that enable analytics, AI/ML, and BI at an organizational scale.
• Pioneer advanced and forward-looking solutions: Introduce novel approaches in real-time processing, hybrid/multi-cloud, and AI/ML integration to transform how data is processed and leveraged across the enterprise.
• Mentor and develop senior technical leaders: Influence Principal Engineers, Engineering Managers, and other Staff Engineers; create a culture of deep technical excellence and innovation.
• Establish cross-org technical standards: Define and enforce best practices for data modeling, pipeline architecture, governance, and compliance at scale.
• Solve the most complex, ambiguous challenges: Tackle systemic issues in data scalability, interoperability, and performance that impact multiple teams or the enterprise as a whole.
• Serve as a strategic advisor to executive leadership: Provide technical insights to senior executives on data strategy, emerging technologies, and long-term investments.
• Represent the organization as a thought leader: Speak at industry events/conferences, publish thought leadership, contribute to open source and standards bodies, and lead partnerships with external research or academic institutions.

Technical Skills:

• 8 years of experience
• Mastery of GCP Data Ecosystem: Deep authority in architecting, designing, and building complex solutions using BigQuery, Dataflow, Dataproc, and Pub/Sub for massive-scale batch and streaming workloads
• Advanced programming and infrastructure capabilities: Expertise in Python, or Java, along with infrastructure-as-code tools like Terraform or Cloud Deployment Manager.
• Leadership in streaming and big data systems: Authority in tools such as BigQuery, Dataflow, Dataproc, Pub/sub for both batch and streaming workloads.
• Enterprise-grade governance and compliance expertise: Design and implement standards for data quality, lineage, security, privacy (e.g., GDPR, HIPAA), and auditability across the organization.
• Build and maintain curated datasets and semantic layers that meet defined contracts (freshness, accuracy, completeness, schema stability).
• Implement scalable data storage patterns (warehouse/lakehouse, partitioning, clustering, file layout, table formats) and performance optimization.
• Engineer secure data access patterns (RBAC/ABAC, row/column-level security, tokenization/masking, encryption, key management).
• Data Quality & Lineage: Implementation of automated data validation and lineage tracking standards to ensure "Single Source of Truth" reliability across diverse business units.
• Cost Optimization (FinOps): Advanced skill in managing cloud spend through granular resource monitoring and the implementation of cost-efficient data lifecycle policies.
• Strategic integration with AI/ML ecosystems: Architect platforms that serve advanced analytics and AI workloads (Vertex AI, TFX, MLflow).
• Exceptional ability to influence across all levels: Communicate technical vision to engineers, influence strategic direction with executives, and drive alignment across diverse stakeholders.
• Recognized industry leader: Demonstrated track record through conference presentations, publications, open-source contributions, or standards development.

Must Have Skills:

• Deep expertise in data architecture, distributed systems, and multi-cloud (GCP, AWS, Azure)
• Python or Java, infrastructure-as-code (e.g. Terraform)
• Big data tools: BigQuery, Dataflow, Dataproc, Pub/Sub (batch + streaming)
• Data governance, privacy, and compliance (e.g. GDPR, HIPAA)
• Systemic Reliability: Shifting from fixing individual bugs to preventing entire classes of incidents through systematic reliability engineering and observability.