Responsibilities: - Translate business needs and architectural guidance into detailed designs, data contracts, and implementation plans that break down large initiatives into actionable engineering tasks with reliable estimates - Create detailed pipeline designs covering schemas, transformations, partitioning, DLT configurations, orchestration, error handling, and observability that align with the platform architecture through close collaboration with the Data Architect - Lead implementation and guide junior engineers on design, coding standards, and best practices - Develop metadata-driven and configuration-driven pipeline patterns that reduce custom code and improve consistency - Make technical decisions that ensure reliability, performance, maintainability, and scalability. Ensure production readiness with monitoring, lineage, alerting, observability, CI/CD and documentation - Define and enforce engineering design patterns, coding standards, testing practices, and operational best practices - Evaluate and incorporate new technologies and Databricks capabilities that improve reliability, performance, or developer productivity - Validate new technologies with the Data Architect and operationalize them through documentation, examples, and enablement - Implement automated data quality checks, rule enforcement, and exception handling - Production support of both an existing and new platform including optimization of jobs, incident tracking and other analysis required for production - Lead resolution of complex production issues and deliver durable root cause fixes - Maintain SLAs for reliability, recovery, idempotency, performance, and cost efficiency - Mentor Level 2–3 engineers through pairing, design guidance, code reviews, and technical coaching Basic Skills: • Bachelor’s degree preferred; equivalent experience accepted • 10+ years in data engineering (12+ without a degree) • 4+ years building production-grade batch/streaming pipelines using PySpark, Spark Structured Streaming, Python, and SQL • Proven experience with data governance, schema evolution, data lineage, and secure access patterns • Proven 2 years’ experience with maintaining and sustaining data pipelines Preferred Skills: • 3+ years hands-on with Databricks (Delta Lake, DLT, Unity Catalog, workflow jobs) within the last 6 years • Experience building metadata-driven or configuration-driven pipelines • Experience with data quality frameworks (DQX, Great Expectations, or equivalent) • Experience with observability, metrics and query performance analysis • Strong Spark optimization Work Conditions: • Team collaborative hours between 8am to 4pm EST • Corporate office/lab environment • Ability to travel 10% of the time

• influence product teams • inform Data Science and Analytics Platform teams • partner with data consumers and products to ensure quality and usefulness of data assets • help strategize measurement, collecting data, and generating insights • understand and improve product experience and engagement • improve efficiency and costs • guide strategy • report to a Data Engineering Manager • learn from your team mentor who are some of the best Data Engineers in the business.

• Define the Data Platform Architecture • Lead end-to-end technical design for CloudZero's next-generation data platform, from event ingestion and stream processing through hot/cold storage and the query layer to the API surface • Document architectural decisions, tradeoffs, and migration strategies with the rigor of an RFC-driven process • Shape and drive every layer of the new architecture: event ingestion, stream processing and enrichment, real-time serving, analytical storage, query layer, and API • Design and deliver CloudZero's real-time data pipeline from ingestion through enrichment to serving • Establish SLOs for throughput, latency, and correctness, and build the operational playbooks that make this system trustworthy enough to replace the batch pipelines our entire product currently depends on • Tackle real-time streaming at scale across thousands of customers simultaneously, with fault tolerance, backpressure awareness, and correctness as non-negotiables • Redesign CloudZero's dimensional cost model to support high-cardinality, multi-dimensional cost attribution without runaway materialization costs • Drive incremental, delta-based materialization strategies using modern open table formats, dramatically reducing expensive full-rebuild jobs and unlocking millions in annual infrastructure savings • Assess CloudZero's current query infrastructure, drive in-flight migrations to completion, and lead the evolution of the query engine layer going forward • Own performance optimization across partition pruning, predicate pushdown, and query planning, and set the vision for how the query layer grows as data volumes scale 10x • Evolve CloudZero's proprietary cost attribution engine from a batch-oriented model to one that assigns complex cost dimensions by team, feature, and customer within seconds of resource usage • Rethink enrichment, data lineage, and correctness guarantees in a streaming context • Partner with product, infrastructure, and analytics engineering to define a multi-year data platform roadmap • Build consensus across engineering leadership on foundational investments including table formats, streaming frameworks, query engines, and schema management • Participate in architecture reviews, contribute to design patterns and best practices, and mentor senior and staff engineers through code review, pairing, and structured feedback • Make everyone around you better, not by directing, but by raising the collective craft

• Design, develop, and maintain scalable data pipelines, ETL/ELT processes, and data integration solutions with moderate independence • Translate business requirements into technical designs and contribute to solution architecture discussions • Build and enhance business-to-business (B2B) integrations and internal data processing workflows • Diagnose and resolve data quality issues, pipeline failures, and performance bottlenecks; propose optimizations • Develop clear documentation for data models, workflows, and engineering solutions • Collaborate with cross-functional teams including data architects, analysts, application developers, and business stakeholders to ensure effective data delivery • Contribute to project workstreams, ensuring tasks are delivered with quality and within timelines • Adhere to and promote engineering best practices, coding standards, and data governance guidelines