AI-Assisted CloudOps Engineer (Databricks & AWS)

SoftServe Inc.; Colombia; On-site; COP 210,601,000 - 291,602,000; Full time

Job summary

SoftServe in Colombia is seeking a hands-on cloud operations specialist to provide L1–L2 support for Databricks and AWS within our GSU ecosystem. You will own day‑to‑day operational support, help keep critical applications healthy, and use AI‑assisted practices to improve troubleshooting.

Responsibilities include monitoring jobs and pipelines, incident resolution within SLAs, collaborating with product teams, and building automation and knowledge resources to reduce manual work.

Qualifications

• 3–5 years of experience in cloud operations, application or data platform support, SRE, or DevOps, with Databricks and AWS as the main platforms.
• Hands-on experience with Databricks jobs and Workflows, clusters, SQL warehouses, Unity Catalog, Spark logs, Spark UI, and familiarity with the bronze, silver, and gold medallion pattern.
• Strong knowledge of SQL, plus Python for debugging and scripting, and AWS services including Lambda, DynamoDB, Cognito, CloudWatch, IAM, and S3.
• Practical experience with integration and API troubleshooting, including logs, traces, browser developer tools, and understanding how React front ends interact with backend APIs.
• Working knowledge of GenAI and LLM applications, including prompts, RAG, tool or function calling, agents, production failure patterns, and AI‑assisted coding or operations tools.
• Experience with ITIL‑aligned incident and request management, including priority matrices, SLAs, escalation paths, and root cause analysis.
• Clear written English and a service‑minded approach, particularly for tickets, runbooks, end‑user communication, and white‑glove support for business users.
• Additional exposure to Databricks Apps, Lakebase, Delta Live Tables or Lakeflow, Databricks Asset Bundles, GitHub Actions, Terraform, ServiceNow, MCP‑based integrations, Semantic Kernel, LangChain, LLM observability, Databricks AI features, SRE practices, or regulated industries is beneficial.

Responsibilities

• Monitor Databricks jobs, workflows, data pipelines, Oracle API ingestion processes, and AWS services, responding to failures, alerts, performance issues, and data quality problems.
• Own incident and service request resolution within defined SLAs, performing approved remediation such as job reruns, cluster restarts, configuration updates, and resolution of known data issues.
• Collaborate with internal and external stakeholders and L3 product teams to troubleshoot end‑to‑end integrations, elevate complex or code‑level issues with clear evidence, and validate implemented fixes.
• Troubleshoot front‑end, API, backend, database, connectivity, and integration issues using logs, traces, Spark UI, browser developer tools, monitoring tools, and system diagnostics.
• Manage Databricks access and permissions across workspaces, Unity Catalog, groups, and service principals while supporting applications and data platforms.
• Use AI‑powered engineering tools such as GitHub Copilot and Cursor to accelerate troubleshooting, scripting, log analysis, and operational documentation.
• Develop automation scripts, operational tooling, runbooks, knowledge articles, known‑error documentation, and repeatable processes that reduce manual support effort.
• Monitor platform health, performance, capacity, cost trends, dashboards, and observability while contributing to root cause analysis, service reviews, backlog grooming, and continuous improvement activities.

Skills

Databricks

AWS

SQL

Python

React APIs

ITIL

SRE/DevOps

GenAI

Lambda

Unity Catalog

Cursor

Tools

GitHub Copilot

Cursor

CloudWatch

Terraform

Job description

SoftServe in Colombia is seeking a hands-on cloud operations specialist to provide L1–L2 support for Databricks and AWS within our GSU ecosystem. You will own day‑to‑day operational support, help keep critical applications healthy, and use AI‑assisted practices to improve troubleshooting.

Responsibilities include monitoring jobs and pipelines, incident resolution within SLAs, collaborating with product teams, and building automation and knowledge resources to reduce manual work.