Senior Software Engineer, Data & Platform Services

Location: Berlin, Germany Remote Status: Fully Remote (#LI-Remote) LivePerson (NASDAQ: LPSN) is a leader in trusted enterprise conversational AI and digital transformation. The world's leading brands use our award-winning Conversational Cloud platform to connect with millions of consumers. We power nearly a billion conversational interactions every month, providing uniquely rich data analytics and safety tools to unlock the power of conversational AI for better business outcomes. Fast Company named LivePerson the #1 Most Innovative AI Company in the world. Position Overview We're looking for a Senior Software Engineer to join our globally distributed Data Platform team in Europe and play a key role in shaping the future of our data ecosystem. In this high-impact individual contributor role, you'll lead the modernization of our core data platform by migrating mission-critical data pipelines from legacy Hadoop, Spark, and Impala environments to a scalable, cloud-native Databricks architecture. You'll have the opportunity to drive technical decisions, influence platform architecture, and build the foundation that powers data across the business. If you're a hands-on engineer who enjoys solving complex technical challenges, building modern data platforms, and making a lasting impact at scale, we'd love to hear from you! You Will: Key Responsibilities & Impact Lead the Modernization of Our Data Platform: Take ownership of migrating mission-critical data pipelines from legacy Hadoop, Spark, and Impala environments to a modern, scalable Databricks ecosystem. You'll re-architect ETL workflows, convert complex SQL, and seamlessly migrate integrations, connections, and secrets. Accelerate Development with AI-Powered Engineering: Leverage cutting-edge AI coding assistants such as Claude and Codex to dramatically accelerate migration efforts. Rather than manually rewriting legacy code, you'll guide AI tools to convert SQL, modernize ETL scripts, untangle dependencies, and generate clean, maintainable code—while applying your engineering expertise to validate architecture, performance, and quality. Build the Next Generation of Data Pipelines: Design, develop, and optimize scalable data workflows using Airflow and Databricks. You'll create resilient, high-performance pipelines that are reliable, cost-efficient, and built to support growing business needs. Champion Data Quality: Ensure the accuracy and consistency of migrated data by implementing comprehensive validation and testing strategies. You'll play a critical role in maintaining data integrity throughout the migration journey. Improve Platform Reliability: Help drive operational excellence by enhancing observability, monitoring, and incident response practices. Contribute to service reliability through SLOs/SLAs and participate in a scalable on-call rotation to support production systems. Deliver Meaningful Business Impact: Apply your understanding of contact center analytics to build platforms that power customer conversation insights, reporting, and business intelligence. You'll embrace a data-driven mindset, designing systems that are measurable, observable, and continuously improving. You Have: Required Skills & Qualifications Extensive Software Engineering Experience: 6–8 years of experience building and supporting distributed systems and large-scale data platforms, with a proven ability to own complex technical initiatives from design through production. Proven Platform Modernization Experience: Hands-on experience leading or playing a key role in migrating enterprise data platforms from legacy technologies—such as Hadoop, Spark, Hive, or Impala—to modern cloud-based solutions like Databricks. Deep Databricks Expertise: Strong knowledge of the Databricks ecosystem, including Jobs, Delta Lake, cluster configuration and optimization, and migrating complex SQL workloads from Impala or Hive to Spark SQL. Data Pipeline & Orchestration Skills: Advanced Python programming skills for ETL development and extensive experience designing, building, debugging, and optimizing production data pipelines and orchestration workflows using Airflow and Databricks. Cloud & Infrastructure Knowledge: Experience working with cloud-native data platforms, particularly Google Cloud Platform (GCP) and Google Cloud Storage (GCS), with an understanding of containerized, Kubernetes-based (GKE) microservice architectures. Modern Data Platform Expertise: Strong understanding of distributed data processing architectures and the trade-offs between real-time and batch data processing. Hands-on experience with legacy big data technologies, including Hadoop and MapReduce, with the ability to modernize and migrate complex data ecosystems. Knowledge of data integration patterns, including APIs, connectors, webhooks, and event-driven architectures. Cloud & Platform Engineering: Experience building, deploying, and managing cloud-native data platforms, with Google Cloud Platform (GCP) experience preferred. Familiarity with containerized environments and running data workloads on Kubernetes. A strong focus on building highly reliable, scalable, and observable platforms, with experience implementing monitoring, validation, and data quality controls to ensure successful migrations. AI-Assisted Software Development: Experience using AI-powered development tools such as Claude, Codex, or similar coding assistants to accelerate software development, code modernization, and platform migrations. Skilled at effectively prompting, reviewing, and validating AI-generated code while ensuring architectural integrity, maintainability, and production readiness. Collaboration & Communication: Excellent communication and collaboration skills, with experience partnering across engineering teams in multiple time zones. Comfortable working in a globally distributed environment with colleagues across the US, Europe, Israel, and India. Nice to Have: Experience working with conversational AI , contact ce

Requirements: Qualifications Extensive Software Engineering Experience: 6–8 years of experience building and supporting distributed systems and large-scale data platforms, with a proven ability to own complex technical initiatives from design through production. Proven Platform Modernization Experience: Hands-on experience leading or playing a key role in migrating enterprise data platforms from legacy technologies—such as Hadoop, Spark, Hive, or Impala—to modern cloud-based solutions like Databricks. Deep Databricks Expertise: Strong knowledge of the Databricks ecosystem, including Jobs, Delta Lake, cluster configuration and optimization, and migrating complex SQL workloads from Impala or Hive to Spark SQL. Data Pipeline & Orchestration Skills: Advanced Python programming skills for ETL development and extensive experience designing, building, debugging, and optimizing production data pipelines and orchestration workflows using Airflow and Databricks. Cloud & Infrastructure Knowledge: Experience working with cloud-native data platforms, particularly Google Cloud Platform (GCP) and Google Cloud Storage (GCS), with an understanding of containerized, Kubernetes-based (GKE) microservice architectures. Modern Data Platform Expertise: Strong understanding of distributed data processing architectures and the trade-offs between real-time and batch data processing. Hands-on experience with legacy big data technologies, including Hadoop and MapReduce, with the ability to modernize and migrate complex data ecosystems. Knowledge of data integration patterns, including APIs, connectors, webhooks, and event-driven architectures. Cloud & Platform Engineering: Experience building, deploying, and managing cloud-native data platforms, with Google Cloud Platform (GCP) experience preferred. Familiarity with containerized environments and running data workloads on Kubernetes. A strong focus on building highly reliable, scalable, and observable platforms, with experience implementing monitoring, validation, and data quality controls to ensure successful migrations. AI-Assisted Software Development: Experience using AI-powered development tools such as Claude, Codex, or similar coding assistants to accelerate software development, code modernization, and platform migrations. Skilled at effectively prompting, reviewing, and validating AI-generated code while ensuring architectural integrity, maintainability, and production readiness. Collaboration & Communication: Excellent communication and collaboration skills, with experience partnering across engineering teams in multiple time zones. Comfortable working in a globally distributed environment with colleagues across the US, Europe, Israel, and India. Nice to Have: Experience working with conversational AI , contact ce

Qualifications Extensive Software Engineering Experience: 6–8 years of experience building and supporting distributed systems and large-scale data platforms, with a proven ability to own complex technical initiatives from design through production. Proven Platform Modernization Experience: Hands-on experience leading or playing a key role in migrating enterprise data platforms from legacy technologies—such as Hadoop, Spark, Hive, or Impala—to modern cloud-based solutions like Databricks. Deep Databricks Expertise: Strong knowledge of the Databricks ecosystem, including Jobs, Delta Lake, cluster configuration and optimization, and migrating complex SQL workloads from Impala or Hive to Spark SQL. Data Pipeline & Orchestration Skills: Advanced Python programming skills for ETL development and extensive experience designing, building, debugging, and optimizing production data pipelines and orchestration workflows using Airflow and Databricks. Cloud & Infrastructure Knowledge: Experience working with cloud-native data platforms, particularly Google Cloud Platform (GCP) and Google Cloud Storage (GCS), with an understanding of containerized, Kubernetes-based (GKE) microservice architectures. Modern Data Platform Expertise: Strong understanding of distributed data processing architectures and the trade-offs between real-time and batch data processing. Hands-on experience with legacy big data technologies, including Hadoop and MapReduce, with the ability to modernize and migrate complex data ecosystems. Knowledge of data integration patterns, including APIs, connectors, webhooks, and event-driven architectures. Cloud & Platform Engineering: Experience building, deploying, and managing cloud-native data platforms, with Google Cloud Platform (GCP) experience preferred. Familiarity with containerized environments and running data workloads on Kubernetes. A strong focus on building highly reliable, scalable, and observable platforms, with experience implementing monitoring, vali