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.br/We 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 pbYour key tasks /b /p 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 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 pbData Modeling Storage /b /p 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 pbData Quality Governance /b /p ul liDefine and implement data quality controls across ingestion and transformation layers /li liDevelop validation, reconciliation, deduplication, and completeness checks /li liSupport data lineage, metadata management, ownership, retention, auditability, and regulatory requirements /li liImplement controls for sensitive and regulated data /li /ul pbPlatform Integration Analytics Enablement /b /p 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 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 wit