Your data lake costs a fortune. Half your pipelines fail.

Enterprise technology companies build data platforms at scales where small inefficiencies compound into large problems. A 5% increase in unnecessary compute across thousands of jobs costs millions. A 50% pipeline failure rate means the data team spends half its time debugging instead of building. Engineers who do not understand the platform create the problems that the platform team has to fix.

These are not problems that a new tool solves. They are infrastructure and process problems that require someone to investigate root causes, classify failure modes, optimize resource utilization, and train the engineering organization to use the platform effectively.

Data platform cost optimization. Enterprise data lakes, analytics warehouses, and log aggregation pipelines consume cloud budgets that keep growing with no clear connection to business value. We profile actual cluster resource utilization, identify wasteful query patterns and misconfigured jobs, and deliver targeted optimizations. Typical results: 30% or greater compute cost reduction through better resource allocation, not by cutting capabilities.

Pipeline reliability improvement. When half your pipelines fail on any given run, the first question is whether the problem is the platform or the users. We build failure classification logic that distinguishes infrastructure errors from user errors, then address each category systematically. We have taken pipeline success rates from 50% to 90% by making root causes visible and fixable.

Engineering team enablement. The most expensive data platform problem is hundreds of engineers using it inefficiently. Bad query patterns, unnecessary full-table scans, and misconfigured jobs create cost and reliability problems that the infrastructure team inherits. We run structured training programs that teach engineers to use the platform correctly. The result is measurable: 40% reduction in pipeline failures after enablement, and an infrastructure team that stops firefighting.

Agentic AI and MCP. As AI agents become standard tooling for engineering teams, the data platform needs to support agent-scale query volumes with proper security and governance. Read our analysis of [what MCP costs, where it breaks, and when to use it](/blog/mcp-enterprise-data-strategy) or listen to our episode on [what MCP means for enterprise data strategy](/podcasts/eventual-consistency/ep-19-mcp-enterprise-data-strategy) for a deep dive on how data teams should prepare.

Platform observability and governance. If you cannot tell whether a failure is a platform issue or a user issue, you cannot prioritize what to fix. We build the classification, monitoring, and alerting layers that give platform teams visibility into what is actually happening and where the leverage is.

Enterprise technology teams do not need another vendor selling a platform. They need someone who will investigate the specific problems in their specific infrastructure and deliver measurable improvements. Root cause analysis, targeted optimization, and engineering enablement — producing results you can measure in cost savings, pipeline success rates, and engineering hours recovered.