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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in architecting and implementing distributed data systems, with a strong focus on high-throughput, low-latency services and real-time data processing. Proficient in managing end-to-end data pipelines and collaborating with cross-functional teams to ensure data accuracy and quality.
Highest-signal resume keywords
Distributed Systems DesignLarge-Scale Data ProcessingApache KafkaPython ProgrammingCloud-Native Architectures
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data Processing SystemsStreaming ConceptsWorkflow OrchestrationGRPC ServicesData WarehousingBatch Pipeline JobsData Quality MonitoringEvent-Time ProcessingStateful Stream ProcessingCheckpointing and Recovery
Soft Skills
CollaborationProblem-SolvingCommunication
Tools & Technologies
Apache FlinkApache SparkAirflowKubernetesDockerBigQuery
Industry Keywords
Ad Interaction DataNegative TargetingData AccuracyPrivacy OperationsEvent Delivery Rates
Tech Stack
Tools & technologiesAirflowApacheBigQueryCloudDistributed SystemsDockerGRPCJavaKafkaKubernetesPythonScalaSparkUnity
About the role
Key responsibilities & impact- Architect and implement distributed data systems for Unity’s near-real-time reporting platform
- Own the end-to-end attribution pipeline from mobile ad interaction through data warehouse and ML/Reporting signals
- Design, build, and operate high-throughput, low-latency services and streaming pipelines
- Process terabytes of ad interaction data, including enrichments, transformations, and privacy operations
- Lead migration from a legacy event-handling layer to a modern gRPC-based service
- Design backfill and failover behavior while preserving data accuracy during cutover
- Improve caching and persistence layers across the ad lifecycle
- Build and own ad-serving services, including negative targeting and block listing
- Implement logging, tracing, authentication, Kafka-backed caching, health checks, readiness probes, and Docker integrations
- Monitor data health, anomalies, event delivery rates, and data quality signals in real time
- Maintain batch pipeline jobs producing daily datasets in BigQuery
- Collaborate with ML, Reporting, and external partners; lead investigation and recovery when issues occur
Requirements
What you’ll need- Strong foundation in distributed systems and systems design
- Hands-on experience building and operating large-scale data processing systems
- Deep understanding of streaming concepts: exactly-once semantics, watermarking and event-time processing, stateful stream processing, checkpointing and recovery, and backpressure handling
- Production experience with frameworks such as Apache Flink, Spark, Kafka, or similar technologies
- Proficiency in Python, Java, or Scala
- Experience with workflow orchestration tools such as Airflow for stream and batch coordination
- Strong understanding of cloud-native architectures and distributed infrastructure including Kubernetes, containerization, and cloud platforms
- Experience migrating legacy systems to modern architectures in production environments
- Sufficient knowledge of English for professional verbal and written exchanges
Benefits
Comp & perks- Comprehensive health, life, and disability insurance
- Commute subsidy
- Employee stock ownership
- Competitive retirement/pension plans
- Generous vacation and personal days
- Support for new parents through leave and family-care programs
- Office food snacks
- Mental Health and Wellbeing programs and support
- Employee Resource Groups
- Global Employee Assistance Program
- Training and development programs
- Volunteering and donation matching program
- Equity awards
- Participation in company incentive plans, such as annual discretionary bonuses or sales commissions