Slalom Flex (Project Based)- Sr. Data Engineer/ Architect

Slalom is a consulting firm seeking a Senior Data Engineer / Data Architect to design, build, and scale modern data platforms for analytics and machine learning. The role involves creating scalable data pipelines, defining data models, and ensuring reliable production-grade data systems while collaborating with cross-functional teams.

Responsibilities:

• Design and implement scalable, high-performance data pipelines using modern data technologies
• Develop and maintain robust data models that support analytics, reporting, and downstream applications
• Build data platforms leveraging tools such as Databricks, Spark, and cloud-native services (AWS or GCP)
• Own end-to-end data workflows including ingestion, transformation, orchestration, and serving layers
• Implement best practices for data quality, testing, observability, and governance across data systems
• Partner closely with product, analytics, data science, and engineering teams to deliver data-driven solutions
• Lead technical design discussions and influence data architecture decisions across platforms
• Mentor engineers and contribute to raising the technical bar across the team [Sr. Python...a Engineer , Word], [Data Engineer , Word]

Requirements:

• Strong hands-on experience with: Python
• Strong hands-on experience with: SQL
• Strong hands-on experience with: Apache Spark
• Strong hands-on experience with: Databricks or similar data platforms
• Proven experience building scalable data pipelines and production-grade data systems
• Deep expertise in data modeling (dimensional, medallion, or similar architectures)
• Experience with modern data stack tools such as: Airflow (or equivalent orchestration tools)
• Experience with modern data stack tools such as: dbt
• Experience with modern data stack tools such as: Cloud platforms (AWS or GCP)
• Strong understanding of data engineering best practices including: Data quality and testing frameworks
• Strong understanding of data engineering best practices including: Observability and monitoring
• Strong understanding of data engineering best practices including: Data governance and lineage
• Experience working cross-functionally with product, analytics, data science, and engineering teams
• Experience building real-time or streaming data pipelines (Kafka, event-driven architectures, etc.)
• Exposure to machine learning systems, including feature pipelines or personalization/recommendation platforms
• Experience working with large-scale behavioral, telemetry, or product data
• Background operating as a technical lead, including architecture ownership and mentoring other engineers
• Experience designing systems that support high-scale, low-latency data processing