Data Engineering Leadership

• Design high-scale APIs for multidimensional reporting and analytical workloads
• Define contracts for metrics, dimensions, filters, schemas, and aggregations
• Improve dynamic query generation, caching, response shaping, and execution performance
• Connect APIs to batch, streaming, and near real-time data sources
• Build primarily in Go or a comparable backend language such as Java
• Improve observability, incident response, reliability, and consumer experience
• Lead initiatives involving Product, Data Platform, ML, and Application Engineering

• Build large-scale Spark pipelines and streaming systems using Kafka and Flink
• Transform raw event data into canonical, production-grade datasets
• Design partitioning, storage, aggregation, and query strategies for enormous datasets
• Support historical backfills, incremental processing, and real-time availability
• Help decompose centralized processing into domain-oriented streams and workloads
• Move appropriate services and processing patterns from Hadoop toward Kubernetes
• Improve data quality, correctness, observability, throughput, and compute efficiency
• Participate in the long-term design of globally distributed data infrastructure

• Set the technical direction for the team’s products, systems, and architectures while remaining comfortable working directly with the code and technology
• Hire, mentor, and develop engineers, creating an environment of accountability, collaboration, and meaningful technical growth
• Partner with Product to establish roadmaps, goals, forecasts, and measurable technical and business outcomes
• Manage scope and sequencing across more opportunities than available resources, communicating progress and risks early and coordinating dependencies across teams
• Own the quality, health, and outcomes of the team’s platforms, including operational response, technical debt, reliability, and continuous improvement

• Platform architecture: Define and evolve patterns across edge, transport, compute, storage, modeling, query execution, and serving
• Batch and streaming systems: Build systems supporting historical processing, backfills, incremental updates, and near real-time availability without creating unnecessary architectural complexity
• Data products and interfaces: Provide stable, flexible access to metrics, dimensions, filters, aggregations, and canonical datasets through well-designed APIs and analytical platforms
• Performance and economics: Optimize query planning, data layout, partitioning, caching, execution, throughput, and infrastructure use so capacity does not need to grow linearly with data volume
• Correctness and trust: Design for deduplication, late-arriving data, schema evolution, data contracts, reconciliation, and the integrity of business-critical reporting and billing datasets
• Reliability and observability: Own monitoring, alerting, incident response, root-cause analysis, workflow health, and the mechanisms that make system behaviour visible
• Cross-functional influence: Work across Data Engineering, Data Systems, Application Engineering, Product, analytics, machine learning, and experimentation teams to turn platform capabilities into business outcomes

• Processing and streaming: Spark, Scala, Kafka, Flink, Airflow
• Data services and APIs: Go, Java, REST, gRPC, SQL
• Compute and storage: Kubernetes, Hadoop/HDFS, Ceph
• Query and analytics: Trino, Vertica, Druid and StarRocks
• Reporting and BI: Looker, Superset, Redash

• Vision and strategic direction: Identify high-leverage technical opportunities and translate them into a coherent direction for the platform and business
• System design and delivery: Lead the design and implementation of distributed data systems operating under heavy workloads and demanding latency requirements
• Data platform evolution: Help move the organization from centralized, batch-oriented systems toward workload-aware architectures combining batch, streaming, and asynchronous processing
• API and query architecture: Define durable interfaces, schemas, query abstractions, and serving patterns for flexible access to large-scale multidimensional data
• Pipeline and model ownership: Build canonical datasets and processing workflows supporting reporting, billing, products, analytics, experimentation, and machine-learning use cases
• Operational excellence: Improve observability, service health, incident response, workflow reliability, technical debt management, and post-incident learning
• Organizational leadership: Coordinate across teams, unblock dependencies, communicate trade-offs clearly, and build alignment around technical and product outcomes

• You have significant experience designing, building, and operating data-intensive systems at scale
• You have a deep understanding of distributed-systems fundamentals, including partitioning, consistency, failure modes, state, throughput, latency, and cost trade-offs
• You have experience with large-scale data processing and modeling (the client leans on Spark currently)
• You have experience with streaming technologies such as Kafka and Flink, including incremental processing, late data, deduplication, and replay
• You have experience designing stable data contracts, schemas, canonical datasets, and transformation layers for business-critical use cases
• You have experience with large-scale storage, data warehouse, or analytical query systems and a strong grasp of query performance, data layout, and workload-aware optimization
• You have experience with workflow orchestration and the production deployment or operation of services on Kubernetes
• You have a track record of end-to-end ownership: navigating ambiguity, debugging complex cross-system issues, making sound trade-offs, and improving systems after they enter production
• You have an excellent command of English and experience collaborating with neighbouring engineering disciplines and stakeholders beyond R&D

• You have 3+ years of experience leading and managing distributed teams
• You have an affinity for mentorship and creating an environment that nurtures a balance of innovation and accountability
• You have experience partnering with Product Management and internal stakeholders to shape roadmaps, forecasts, deliverables, timelines, and measurable outcomes
• You are experienced making tradeoffs and experience exercising judgment to prioritize a broad portfolio when initiatives outnumber resources, including thoughtful sequencing and proactive communication of progress, delays, and risk

Compensation