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

FEQ327R420

As a Specialist Solutions Architect (SSA) – Data Engineering & Warehousing, you will guide strategic enterprise customers through cloud data engineering transformations across a wide variety of mission-critical use cases.

In this customer-facing role, you will collaborate with and support Solutions Architects by leveraging your hands-on production experience with large-scale data engineering and lakehouse architecture. You will help organizations navigate technical evaluations, optimize business intelligence and analytics workloads, and align their technical roadmaps with the Databricks Data Intelligence Platform.

Reporting to the Specialist Field Engineering Manager, you will serve as a deep domain expert while continuing to strengthen your technical leadership through mentorship, continuous learning, and specialized training programs.

This position can be remote.

• Guide Strategic Implementations: Provide technical leadership to help enterprise customers successfully build, scale, and optimize big data and large-scale data warehousing workloads.

• Prove Platform Value: Architect production-ready pipelines and demonstrate the power of the Databricks Data Intelligence Platform through end-to-end performance testing, load testing, and optimization.

• Deep Domain Expertise: Build expertise across specialized domains such as data lake architecture, high-velocity streaming, automated ingestion workflows, and data observability.

• Support Technical Sales: Partner with Solutions Architects on complex pre-sales engagements, including custom proofs of concept (POCs), workload sizing estimations, and custom architecture designs.

• Community & Adoption: Enable adoption by leading workshops, hackathons, and conference presentations, while actively contributing to the broader Databricks community.

• 5+ years of experience in a technical role with deep expertise across:

• Data & Software Engineering: Hands-on experience with streaming technologies (e.g., Spark Streaming, Kafka), batch ingestion, performance tuning, and troubleshooting complex Spark workloads.

• Data Applications Engineering: Experience building or supporting data-driven use cases, predictive analytics pipelines, or customer analytics platforms.

• Data Warehousing & Migration: Experience migrating EDW workloads (e.g., legacy SQL, Redshift, Snowflake, Synapse, EMR) across OLAP/OLTP systems; advanced query tuning, governance, and MPP debugging.

• Data Observability & Security: Telemetry, high-velocity log ingestion, anomaly detection, and familiarity with SIEM tools (e.g., Splunk, Elastic, Sentinel).

• Deep understanding of modern lakehouse architectures (Delta Lake, data modeling, BI integration) across major cloud platforms (AWS, Azure, or GCP).

• Production-level programming experience in SQL and at least one language among Python, Scala, or Java. [Preferred] Prior experience in a pre-sales or post-sales technical consulting role.

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or