Specialist Solutions Architect - Data Engineering & Warehousing (Financial Services)

Experteer Overview In this remote, customer-facing role you guide enterprise customers through cloud data engineering transformations, leveraging hands-on experience with large-scale data warehousing and lakehouse architectures. You collaborate with Solutions Architects to validate platform value through end-to-end testing and optimization, while developing domain expertise in data lakes, streaming, ingestion, and observability. You influence technical roadmaps and contribute to community adoption and pre-sales efforts. Compensation / Benefits * Provide technical leadership to build, scale, and optimize big data and data warehousing workloads * Architect production-ready pipelines and demonstrate platform value through performance testing and optimization * Develop deep domain expertise in data lake architectures, streaming, ingestion workflows, and data observability * Support pre-sales engagements with custom POCs, workload sizing, and architecture designs * Drive community adoption via workshops, hackathons, and conference talks Tasks * 5+ years in a technical role with deep data engineering and warehousing expertise * Hands-on experience with streaming tech (Spark Streaming, Kafka) * Experience with batch ingestion, performance tuning, and debugging complex Spark workloads * Experience migrating EDW workloads across OLAP/OLTP systems (e.g., Redshift, Snowflake, Synapse, EMR) * Knowledge of data observability, telemetry, anomaly detection, and SIEM tools (Splunk, Elastic, Sentinel) * Deep understanding of modern lakehouse architectures (Delta Lake) across AWS, Azure, or GCP * Production-level programming in SQL and at least one language (Python, Scala, or Java) * Bachelor’s degree in a related field or equivalent practical experience * Willingness to travel up to 30% * Preferred: prior experience in pre-sales or post-sales technical consulting Key requirements *

via workshops, hackathons, and conference talks Tasks * 5+ years in a technical role with deep data engineering and warehousing expertise * Hands-on experience with streaming tech (Spark Streaming, Kafka) * Experience with batch ingestion, performance tuning, and debugging complex Spark workloads * Experience migrating EDW workloads across OLAP/OLTP systems (e.g., Redshift, Snowflake, Synapse, EMR) * Knowledge of data observability, telemetry, anomaly detection, and SIEM tools (Splunk, Elastic, Sentinel) * Deep understanding of modern lakehouse architectures (Delta Lake) across AWS, Azure, or GCP * Production-level programming in SQL and at least one language (Python, Scala, or Java) * Bachelor’s degree in a related field or equivalent practical experience * Willingness to travel up to 30% * Preferred: prior experience in pre-sales or post-sales technical consulting Key requirements *