Principal Engineer, Data
$148,800–$223,200 year
RemoteUnited States
Job Summary
Assess cross-organizational data pipelines to identify inefficiencies, technical debt, and risks, then architect scalable, secure, and cost-efficient solutions on Azure and Databricks. Act as the technical escalation point for complex reliability issues, defining architectural guardrails and standards that balance standardization with team agility. Enable AI and advanced analytics workloads by establishing feature readiness, data quality thresholds, and observability while partnering with data science and product teams. Maintain the data engineering technology roadmap, running proofs of concept to validate emerging tools and presenting strategic trade-offs to executive stakeholders. Optimize platform costs through FinOps practices and drive cross-functional influence to align architectural decisions with business goals without formal management authority.
Required Qualifications
- 10+ years of relevant job experience
- Deep experience designing and reviewing SQL-based data models and transformations at enterprise scale
- 3+ years working with big data technologies including but not limited to Databricks, SPARK, Azure
- Excellent understanding of engineering fundamentals: testing automation, code reviews, telemetry, iterative delivery and DevOps
- Experience with polyglot storage architectures including relational, columnar, key-value, graph or equivalent
- Demonstrated ability to communicate effectively to both technical and non-technical, globally distributed audiences
- Solid foundations in formal architecture, design patterns and best practices
- Experience designing data platforms that support AI/ML, advanced analytics, or intelligent automation workloads
- Deep experience with Azure cloud services and Databricks, including Spark-based data engineering patterns
- Proven ability to translate business needs into scalable data solutions
- Experience owning or operating large-scale cloud data platforms, including responsibility for cost optimization, performance tuning, and platform reliability
- Demonstrated ability to scale data engineering through enablement, standards, and platforms, not just direct delivery
- Must be inquisitive and demonstrate openness to innovation including AI
- This is a remote position; however, candidates must be willing and able to travel to and work onsite at client, temporary, or corporate office locations as business needs require
Desired Qualifications
- Strong understanding of Databricks pricing models, workload optimization, and Azure cloud cost drivers preferred
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