Senior Data Platform Engineer

Founded 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.

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.

We 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.

This 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.

The 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.

Responsibilities

Design and evolve scalable data platform architectures for security, audit, operational, and analytics data; Define data storage strategies, schemas, data models, partitioning, retention, and lifecycle management approaches; Evaluate and prototype new technologies and architectures to improve scalability, performance, and cost efficiency; Design, build, and maintain batch and streaming data pipelines; Develop robust ingestion frameworks for logs, audit data, application events, security telemetry, and operational datasets; Implement data transformation, enrichment, normalization, correlation, and aggregation processes; Ensure pipelines are reliable, scalable, observable, and resilient; Design relational, analytical, and event-based data models; Optimize database structures, query performance, indexing, and storage efficiency; Support the implementation of data lake, warehouse, and lakehouse concepts where appropriate; Define and implement data quality controls across ingestion and transformation layers; Develop validation, reconciliation, deduplication, and completeness checks; Support data lineage, metadata management, ownership, retention, auditability, and regulatory requirements; Implement controls for sensitive and regulated data; Integrate data from diverse internal and external platforms, applications, databases, APIs, and messaging systems; Deliver curated datasets that support reporting, analytics, observability, compliance, and security operations; Support integration with SIEM, monitoring, and business intelligence platforms; Collaborate with analytics and reporting teams to improve data accessibility and usability; Develop data engineering services, tooling, and automation using Python and SQL; Contribute to CI/CD practices for data platform components; Support infrastructure automation where required, using Terraform and related tooling; Maintain engineering standards, documentation, and operational procedures

Requirements

8+ years of experience in Data Engineering, Data Platform Engineering, Database Engineering, or a related field; Strong SQL expertise, including schema design, data modeling, query optimization, indexing, and performance tuning; Experience designing and operating production-grade batch and/or streaming data pipelines; Experience with large-scale data platforms and analytical data architectures; Strong proficiency in Python and SQL; Experience integrating data from multiple sources, platforms, APIs, and event streams; Strong understanding of data quality, schema evolution, lineage, governance, and lifecycle management; Experience with relational databases and analytical storage technologies; Familiarity with CI/CD concepts and Git-based development practices; Strong analytical, problem-solving, and troubleshooting skills; Experience working with sensitive, security-relevant, or regulated data

Nice to have

Experience with Kafka or other streaming and messaging technologies; Hands-on administration and search query development with Splunk (SPL) or alternative SIEM/observability stacks (Elasticsearch/Logstash/Kibana, Datadog); Experience with modern data platform technologies such as Apache Iceberg, Delta Lake, Apache Hudi, Trino, Spark or Parquet; Experience with data lakehouse architectures; Experience implementing data quality frameworks and data governance controls; Familiarity with data cataloging, lineage, and metadata management solutions; Experience integrating data platforms with analytics tools such as Apache Superset, Power BI, Tableau, or Metabase; Experience in banking, fintech, cybersecurity, or other regulated industries; Working knowledge of Terraform and cloud-based data platforms; Security telemetry and audit-event processing; SIEM and observability integrations; Compliance and regulatory reporting datasets; AI/ML-ready data platform architectures; Real-time analytics and event-driven architectures