Data Engineer- Databricks

Slalom is a global business and technology consulting company focused on delivering practical solutions that drive meaningful impact. They are seeking a hands-on Data Engineer to build and improve data pipelines while ensuring the smooth operation of production platforms in a modern Databricks + Snowflake + Azure Data Factory ecosystem.

Responsibilities:

• Build, enhance, and troubleshoot Azure Data Factory (ADF) pipelines and Snowflake ELT/ETL processes, and Databricks ELT/ETL
• Monitor daily/weekly/monthly data batch cycles and respond quickly to job failures, latency, or data-quality issues
• Diagnose and resolve production incidents within Service-Level Agreements (SLA) targets using SQL, Python , platform logs, documentation, and runbooks
• Perform root cause analysis (RCA) , write documentation of findings, and implement fixes to prevent recurrence of issues
• Drive operational improvements : automation, alert tuning, performance optimization, and reliability enhancements
• Work within ITIL/ITSM workflows (ticketing, incident/problem management, change processes) and communicate clearly with both technical and business stakeholders
• Collaborate with teammates across regions—sharing context early, documenting decisions, and maintaining strong handoffs
• Apply agentic AI in day-to-day E&O delivery using Snowflake Cortex and agentic coding tools to investigate incidents, accelerate root cause analysis, navigate code and data context, automate repeatable work, and turn operational knowledge into reusable skills
• Use agentic tools to support, not replace, engineering judgment by verifying evidence, challenging recommendations, protecting production data, and clearly communicating what the agent did, what was validated, and what still requires human approval

Requirements:

• A proactive, data platform service-focused mindset: you will own incidents, issues, and development work end-to-end and follow your work through to resolution
• A Problem-solving mindset; Willingness to learn and understand how issues and resolutions fit into the EDW system design
• Experience in working independently on assigned work; but also, comfortable collaborating with others to investigate and resolve issues; comfortable sharing concepts and information about issue resolution with teammates
• 2-3 years of experience in data engineering, ETL/ELT development, or production support for data platforms
• Strong SQL skills (debugging, performance awareness, data validation)
• Strong Python skills (troubleshooting, scripting, automation)
• Hands-on experience with Snowflake and/or Databricks and Azure Data Factory (ADF)
• Comfort working in a fast-paced, consulting-oriented environment where priorities shift and outcomes matter
• An agentic-assisted engineering mindset: comfort using AI agents as part of daily engineering work by providing clear context and constraints, reviewing proposed plans, SQL, and code, validating outputs against source evidence, and iterating when results are incomplete or unsafe
• Strong engineering fundamentals for AI-enabled operations, including Git and pull-request practices, scripting or Python experience, API and integration literacy, and the ability to trace issues across data pipelines, logs, code repositories, and operational records
• Experience with ServiceNow, Control-M, Azure DevOps, Git-based workflows, and/or CI/CD
• Exposure to data observability/monitoring patterns, alerting, and platform logging
• Familiarity with modern data tooling (e.g., dbt) or cloud data ecosystem patterns
• Experience with Snowflake Cortex or enterprise GenAI platforms, including Cortex Agents, Cortex Search, Cortex Analyst, LLM functions, or a comparable enterprise agent or GenAI platform
• Experience building reusable agent solutions, including agent skills, tool integrations, prompts, or automations for repeatable engineering and operations workflows
• Experience with responsible AI operations, including retrieval-augmented generation (RAG) or governed knowledge, evaluation and observability, access controls, human-in-the-loop approvals, and safe production rollout