Senior Data Platform Engineer

ppFounded and headquartered in Switzerland, Avaloq is continuously expanding its global footprint with around 2,500 colleagues in 12 countries, and more than 170 clients in 35 countries. We are an industry-leading provider of wealth management technology and services for financial institutions around the world, including private banks and wealth managers, investment managers, as well as retail and neo banks. Our research led approach and continual innovation is powered by the passion and creativity of our colleagues. /p pWe are always looking for talented people to join us on our mission to orchestrate the financial ecosystem and democratize access to wealth management. Avaloq offers the opportunity to work closely with some of the world’s leading financial institutions as we jointly develop and shape careers. Championing a collaborative, supportive and flexible work environment empowers our colleagues to reach their full potential. /p h3Job Description /h3 pWe are seeking a Senior Data Platform Engineer to design, build, and operate a scalable, secure, and governed data platform supporting security, audit, operational, and analytics workloads. /p pThis role is primarily focused on data engineering and data platform development, including data architecture, ingestion pipelines, data modeling, quality controls, governance, and integration with analytics and security platforms. The successful candidate will have strong experience designing and operating data platforms, building reliable batch and streaming pipelines, and managing large volumes of structured and semi-structured data. /p pThe role requires close collaboration with security, infrastructure, and application teams to deliver trusted, high-quality data that enables monitoring, reporting, threat detection, compliance, and business insights. /p h3Your key tasks /h3 h3Data Platform Architecture /h3 ul liDesign and evolve scalable data platform architectures for security, audit, operational, and analytics data /li liDefine data storage strategies, schemas, data models, partitioning, retention, and lifecycle management approaches /li liEvaluate and prototype new technologies and architectures to improve scalability, performance, and cost efficiency /li /ul h3Data Engineering Pipelines /h3 ul liDesign, build, and maintain batch and streaming data pipelines /li liDevelop robust ingestion frameworks for logs, audit data, application events, security telemetry, and operational datasets /li liImplement data transformation, enrichment, normalization, correlation, and aggregation processes /li liEnsure pipelines are reliable, scalable, observable, and resilient /li /ul h3Data Modeling Storage /h3 ul liDesign relational, analytical, and event-based data models /li liOptimize database structures, query performance, indexing, and storage efficiency /li liSupport the implementation of data lake, warehouse, and lakehouse concepts where appropriate /li /ul h3Data Quality Governance /h3 ul liDefine and implement data quality controls across ingestion and transformation layers /li liDevelop validation, reconciliation, deduplication, and completeness checksSupport data lineage, metadata management, ownership, retention, auditability, and regulatory requirements /li liImplement controls for sensitive and regulated data /li /ul h3Platform Integration Analytics Enablement /h3 ul liIntegrate data from diverse internal and external platforms, applications, databases, APIs, and messaging systems /li liDeliver curated datasets that support reporting, analytics, observability, compliance, and security operations /li liSupport integration with SIEM, monitoring, and business intelligence platforms /li liCollaborate with analytics and reporting teams to improve data accessibility and usability /li /ul h3Engineering Automation /h3 ul liDevelop data engineering services, tooling, and automation using Python and SQL /li liContribute to CI/CD practices for data platform components /li liSupport infrastructure automation where required, using Terraform and related tooling /li liMaintain engineering standards, documentation, and operational procedures /li /ul h3Qualifications /h3 ul li8+ years of experience in Data Engineering, Data Platform Engineering, Database Engineering, or a related field /li liStrong SQL expertise, including schema design, data modeling, query optimization, indexing, and performance tuning /li liExperience designing and operating production-grade batch and/or streaming data pipelines /li liExperience with large-scale data platforms and analytical data architectures /li liStrong proficiency in Python and SQL /li liExperience integrating data from multiple sources, platforms, APIs, and event streams /li liStrong understanding of data quality, schema evolution, lineage, governance, and lifecycle management /li liExperience with relational databases and analytical storage technologies /li liFamiliarity with CI/CD concepts and Git-based development practices /li liStrong analytical, problem-solving, and troubleshooting skills /li liExperience working with sensitive, security-relevant, or regulated data /li /ul h3Preferred Qualifications /h3 ul liExperience with Kafka or other streaming and messaging technologies /li liHands-on administration and search query development with Splunk (SPL) or alternative SIEM/observability stacks (Elasticsearch/Logstash/Kibana, Datadog) /li liExperience with modern data platform technologies such as Apache Iceberg, Delta Lake, Apache Hudi, Trino, Spark or Parquet /li liExperience with data lakehouse architectures /li liExperience implementing data quality frameworks and data governance controls /li liFamiliarity with data cataloging, lineage, and metadata management solutions /li liExperience integrating data platforms with analytics tools such as Apache Superset, Power BI, Tableau, or Metabase /li liExperience in banking, fintech, cybersecurity, or other regulated industries /li liWorking knowledge of Terraform and cloud-based data platforms /li /ul h3It would be a real bonus if you have /h3 ul liSecurity telemetry and audit-event processing /li liSIEM and observability integrations /li liCompliance and regulatory reporting datasets /li liAI/ML-ready data platform architectures /li liReal-time analytics and event-driven architectures /li /ul h3Additional Information /h3 pWe realize that managing work life balance is a challenge we all face in our daily lives and in order to support with this we are pleased to offer hybrid and flexible working for most of our Avaloqers to maintain work life balance and still continue our fantastic Avaloq culture in our global offices. /p pIn Avaloq we are proud to embrace diversity and understand the success of our business is built on the power of different opinions, we are whole heartedly committed to fostering an equal opportunity environment and inclusive culture where you can be your true authentic self. /p pWe hire, compensate and promote regardless of origin, age, gender identity, sexual orientation or any other fantastic traits that make us all unique, we have done our best to write this advert in an inclusive and neutral way. /p pPlease be aware that we will not accept speculative CV submissions for any of our roles from recruitment agencies, and any unsolicited candidate submissions will be exempt from any payment expectations. /p /p #J-18808-Ljbffr