# Sr. Specialist Solutions Architect - Data Engineering & Warehousing

**Company:** [Databricks](https://hotfix.jobs/companies/databricks)
**Location:** Remote
**Role:** Solutions Architecture
**Salary:** $219k – $301k/yr
**Experience:** 8+ years
**Skills:** Spark, Kafka, Python, Scala, Java, SQL, AWS, Azure, GCP, Delta Lake, Redshift, Snowflake, Synapse, Emr, Splunk
**Posted:** 2026-06-27

> Guide strategic customers through cloud data engineering and warehousing transformations, architecting production pipelines and lakehouse solutions on the Databricks platform. Requires 8+ years of hands-on data engineering experience with Spark, SQL, Python/Scala/Java, and cloud platforms.

## Job Description

## Impact

- Provide technical leadership to guide strategic customers to successful implementations on big data projects and large-scale data warehousing workloads.
- Prove the value of the Databricks Intelligence Platform for customer workloads by architecting production workloads, including end-to-end pipeline load performance testing and optimization.
- Architect production-level data pipelines, including end-to-end pipeline load performance testing and optimization.
- Become a technical expert in an area such as data lake technology, big data streaming, or big data ingestion and workflows.
- Assist Solution Architects with more advanced aspects of the technical sale, including custom proof of concept content, estimating workload sizing, and custom architectures.
- Provide tutorials and training to improve community adoption (including hackathons and conference presentations).
- Contribute to the Databricks Community.

## Requirements

- 8+ years of experience in a technical role with deep expertise across the following areas:
  - Software / Data Engineering: Hands-on experience with data ingestion, streaming technologies (e.g., Spark Streaming, Kafka), performance tuning, troubleshooting, and debugging Spark or other big data solutions.
  - Data Applications Engineering: Experience building data-driven use cases, such as risk modeling, fraud detection, and customer lifetime value (LTV).
  - Data Warehousing: Advanced query tuning, troubleshooting, data governance, and debugging MPP data warehouses or big data solutions. Experience migrating workloads from EDW systems (e.g., traditional SQL, Redshift, Snowflake, Synapse, EMR) across OLAP & OLTP workloads.
  - Data Observability: Experience with SIEM tools (e.g., Splunk, Elastic, Sentinel), telemetry/high-velocity log ingestion, and anomaly detection.
- Proven track record of maintaining, scaling, and extending production data systems to evolve with complex business needs.
- Deep expertise across multiple core data engineering domains, including:
  - Designing and scaling cost-efficient, high-performance data workloads (ETL/ELT, analytics) in cloud environments.
  - Building and migrating large-scale data pipelines, including batch, CDC (Change Data Capture), and streaming ingestion.
  - Migrating on-premises or Hadoop-based data systems to modern cloud platforms (AWS, Azure, GCP).
  - Developing and managing modern lakehouse and warehouse systems, including Delta Lake technologies, data modeling, governance, and BI integration.
- Production programming experience in SQL and at least one of the following: Python, Scala, or Java.
- Strong familiarity with cloud infrastructure providers (AWS, Azure, or GCP) is highly desirable.
- Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent professional experience.
- Ability to meet expectations for technical training and role-specific milestones within 6 months of hire.
- Willingness to travel up to 30% as needed.

## Nice-to-Haves

- Prior customer-facing experience in a pre-sales or post-sales technical role.

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