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Public cloud spending is on a steep curve, rising from $595.7 billion in 2024 to $723.4 billion in 2025, and the fastest growing line items are often the ones nobody “owns” until the invoice lands. Data platforms sit right in that blast radius: they power dashboards, experiments, and revenue reporting, but they also quietly accumulate permissions sprawl, brittle pipelines, and clusters that run long after the work is done.
Beverly D’souza, a data engineer at Patreon, has built her career inside those practical tensions, and her work as a Business Intelligence Awards judge reinforces the habit that matters most in governance: claims only count when the evidence holds up under scrutiny. To understand how data teams are tightening cost, quality, and release discipline without slowing the business, we spoke with D’souza.
FinOps only works when it shows up in day to day behavior, not in a quarterly postmortem. In Flexera’s 2025 State of the Cloud report, 84% of organizations rank managing cloud spend as a top cloud challenge, and 17% say cloud spend is exceeding budget. The bill arrives either way. What separates calm teams from frantic ones is whether governance lives in a policy document, or in the same screens people use to do the work.
One afternoon, she traced a “small” cost anomaly to a cluster that had quietly become everyone’s default, and nobody’s responsibility. At Patreon, D’souza took ownership of a Databricks warehouse environment that had drifted into escalating costs and messy usage patterns. She implemented a streamlining program that delivered $3M+ in annual savings and drove a 40% cost reduction, pairing cost control with clarity about who needed which resources and why. Using AWS Boto3, Databricks APIs, and Mode, she built a cost and usage view that made spend legible at the point of action, then backed it with a cluster management system that cut daily spend by over 80% on the most expensive clusters. It was not a one time cleanup. It became a shared operating habit.
“If cost is invisible, it becomes political,” says D’souza. “When it is visible in the tools people already use, it becomes fixable.”
Cost control is easier when the foundation is stable, because instability creates “shadow systems” that waste time and money. That is why warehouse migrations have become a mainstream event, not a back office upgrade. The Data Warehouse as a Service market is estimated at $8.55 billion in 2025 and is forecast to reach $18.38 billion by 2029, reflecting how many organizations are still modernizing the core layer where reporting and decision support actually happen. If the platform is slow, people route around it. That is how governance gets undermined.
D’souza saw that pattern firsthand at Bankrate after its acquisition by Red Ventures, when the business had growing traffic but lacked production grade data infrastructure. She orchestrated a migration of 300TB+ from Snowflake to Redshift and rebuilt the surrounding platform to support a site serving around 10M users. To make the move real, not cosmetic, she streamlined 50+ Spark batch and streaming pipelines and rebuilt ingestion so the warehouse could keep up with real traffic, not just scheduled refreshes. She then pushed performance tuning techniques that improved query performance by more than 80% across Snowflake and Redshift.
“A migration is only finished when the same questions stop repeating,” notes D’souza. “If people still do ad hoc extracts because the warehouse feels unreliable, you did not move the business. You just moved storage.”
Once cost and performance are under control, quality becomes the line teams stop crossing. It is also where trust breaks fastest, because it breaks quietly. In a Monte Carlo survey on reliable AI, 68% of respondents said they were not completely confident in their data quality. When a metric is wrong, the downstream impact is not just a bad dashboard. It is a decision that cannot be defended.
At Patreon, D’souza led the design and implementation of a Python Data Quality Framework that reduced regressions by over 50% by testing discrepancies before and after the transformation layer. The point was not to create another gating checklist. It was to catch the kinds of mismatches that only show up when real users pull real numbers under deadline pressure. That experience also shaped her writing on production data hygiene in DZone, where she has published deep technical walkthroughs on ETL validation and anomaly detection, because the fastest way to scale good habits is to make them teachable.
“Most ‘data quality’ failures are really repeatability failures,” says D’souza. “If the checks do not run the same way every time, you are not building trust, you are building hope.”
Quality work is incomplete if the tracking itself becomes a liability. As data teams instrument more user behavior and commerce flows, governance turns into a partnership with legal and privacy groups, not a last minute review. Privacy reviews are part of shipping. D’souza learned that in the WhatsApp Business organization, where instrumentation needed to work across regions with different restrictions and expectations, while still giving product and engineering teams the visibility to iterate.
For the WhatsApp SMB Collections feature, she built the data infrastructure that enabled data driven decision making for the rollout, coordinating with product, design, engineering, and data science to define what needed to be tracked and why. She drove foundational datasets and monitoring that tracked release and adoption across 200M+ business users, and she established reporting that supported phased rollouts without turning every question into an emergency request.
“If you want real adoption signals, you have to earn them,” states D’souza. “That means building tracking that is useful, and building it in a way legal teams can actually approve.”
The last mile of governance is operational: how changes move from idea to production without breaking cost controls, quality guarantees, or privacy constraints. That is where modern teams are investing, and the projections show it. The global continuous integration tools market was valued at $970.52 million in 2022 and is projected to reach $4.377 billion by 2031, a signal that organizations increasingly treat release discipline as core infrastructure, not optional process.
At Bankrate, D’souza helped turn that idea into a working system by moving 20+ business critical ETL processes into CI/CD from ad hoc script deployments, while also migrating manually managed resources into Terraform so infrastructure changes were versioned, reviewed, and repeatable. She integrated a CI/CD process using GitHub and CircleCI that became the central path for data engineering code changes and deployments, then reinforced it with workflow practices like sprint planning and retrospectives so the team could actually see work in flight and failure patterns over time. In Hackernoon, she has written about why dashboards can lie when the underlying payloads and validation are ignored, because the real governance question is not whether a chart looks clean, it is whether the pipeline that produced it can survive contact with reality.
“The goal is not perfect data,”says D’souza. “The goal is a system where cost, quality, and releases stay explainable as the company scales.”
