Director of Engineering (Data Infrastructure)

(P-1490)

Databricks processes petabytes of data and billions of transaction events daily
- every cluster launch, every query executed, every dollar billed flows through
infrastructure that must never fail. When we process billions in billing
transactions with 99.999% accuracy requirements, when we ingest terabytes per
second across 100+ regions, when a five-minute outage costs millions in revenue
and customer trust - infrastructure isn't just important, it's existential. The
next phase of our growth demands disaster recovery systems that prove
reliability rather than hope for it, testing frameworks that catch
production-scale problems before deployment, correctness guarantees that make
billing errors structurally impossible, and automation that scales operations
sublinearly with growth.

In this leadership opportunity, you will build the data infrastructure
organization that makes Databricks' continued growth possible. You'll establish
foundational teams in Bengaluru owning the bedrock systems that guarantee
billing correctness, operational resilience, and zero-downtime recovery across
our entire monetization stack, alongside multi-region data ingestion, developer
platforms, and deployment automation that eliminate friction at petabyte scale.
This isn't about maintaining what exists; it's about architecting the
infrastructure that enables Databricks to scale while reducing operational
burden. You'll define what world-class infrastructure looks like for the next
decade of data platforms.

You will pursue these challenges as a founding technical leader in our
fastest-growing engineering hub and strategic partner to global infrastructure
leaders. In addition to building world-class teams, you will shape architectural
decisions that ripple across the company and champion infrastructure-as-product
thinking that transforms infrastructure into force multipliers globally. You'll
work in an engineering culture born from Apache Spark and open source, where
technical depth matters and infrastructure engineers are celebrated as
craftspeople.

The perfect candidate has built infrastructure organizations at companies where
five nines weren't simply aspirational, where petabyte-scale wasn't marketing
but Monday, and where the infrastructure team's technical leverage determined
whether the business could scale or stall. You have the technical depth to
debate data architecture, the strategic vision to define multi-year platform
roadmaps, the leadership craft to build teams that top engineers want to join,
and most importantly, the conviction that data infrastructure done right doesn't
just support the business; it defines what's possible.

The impact you’ll have:

* Deliver the infrastructure vision for systems processing billions in daily
billing transactions with zero tolerance for error, building disaster
recovery that's provably reliable, testing frameworks that catch what
production sees, correctness systems that make billing errors structurally
impossible, and observability that predicts failures before they happen
* Build Bengaluru's data infrastructure organization by establishing it as the
destination for India's top infrastructure talent, hiring multiple
engineering managers who become force multipliers, and creating a culture
where solving hard distributed systems problems at scale is the daily work
* Own business-critical systems operating 24/7/365 across 100+ regions where
even 99.9% uptime means hours of customer pain, driving reliability
improvements that prevent millions in revenue loss while eliminating
operational toil through frameworks that make systems self-healing,
self-tuning, and self-documenting
* Ship platforms that compound engineering leverage across Databricks:
correctness frameworks that catch billing errors before customers do,
deployment automation that makes regional expansion push-button, data
integration systems that process petabyte-scale flows without human
intervention, and testing infrastructure where comprehensive coverage is
automatic, not heroic
* Position infrastructure as product by treating internal engineering teams as
customers with SLAs, measuring adoption and satisfaction, iterating based on
feedback, and demonstrating that every dollar invested in infrastructure
returns multiplicative gains in product velocity, reliability improvements,
or cost reductions

What you’ll need:

* 14+ years in distributed systems engineering with 6+ years leading
infrastructure organizations and 4+ years managing managers at companies
where infrastructure failures meant immediate revenue impact, customer
escalations, or regulatory consequences - and you built the systems and teams
that made those failures rare
* Technical depth across petabyte-scale data pipelines and distributed systems
reliability where you can engage from "how should we architect multi-region
disaster recovery" to "why is this Kafka cluster exhibiting this latency
pattern" while knowing when to coach versus when to decide
* Track record defining multi-year infrastructure vision and translating it
into sequential deliverables that show value quarterly while building toward
architectural end states, positioning infrastructure investments as business
enablers rather than cost centers, and making build-vs-buy decisions that
compound over time
* Experience building 99.999%+ reliable systems with established practices for
SLOs/SLIs, chaos engineering, disaster recovery, and sophisticated
observability that predicts failures before they happen
* Proven ability to scale infrastructure organizations in high-growth
environments where you've doubled engineering while maintaining quality bar,
developed engineering managers, and created teams where retention is high
because the problems are interesting and the culture is strong
* Communication skills to make complex infrastructure decisions legible to
executives (translating technical investments into business outcomes),
influence cross-functional partners without authority, build trust across
global teams in different timezones with different working styles, and
represent Databricks' technical brand externally
* BS in Computer Science or Engineering; MS or Ph.D. preferred. Experience with
Apache Spark, Delta Lake, large-scale data infrastructure, fintech/billing
systems, or leading infrastructure through hypergrowth strongly preferred

About Databricks

Databricks is the data and AI company. More than 10,000 organizations worldwide
— including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 —
rely on the Databricks Data Intelligence Platform to unify and democratize data,
analytics and AI. Databricks is headquartered in San Francisco, with offices
around the globe and was founded by the original creators of Lakehouse, Apache
Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter
[https://twitter.com/databricks], LinkedIn
[https://www.linkedin.com/company/databricks] and Facebook
[https://www.facebook.com/databricksinc].

Benefits

At Databricks, we strive to provide comprehensive benefits and perks that meet
the needs of all of our employees. For specific details on the benefits offered
in your region click here
[https://docs.google.com/document/d/154un3e8Xav4BceOSlcYFZRGEuQI54xMxVydRwQn54eQ/edit?usp=sharing].

Our Commitment to Diversity and Inclusion

At Databricks, we are committed to fostering a diverse and inclusive culture
where everyone can excel. We take great care to ensure that our hiring practices
are inclusive and meet equal employment opportunity standards. Individuals
looking for employment at Databricks are considered without regard to age,
color, disability, ethnicity, family or marital status, gender identity or
expression, language, national origin, physical and mental ability, political
affiliation, race, religion, sexual orientation, socio-economic status, veteran
status, and other protected characteristics.

Compliance

If access to export-controlled technology or source code is required for
performance of job duties, it is within Employer's discretion whether to apply
for a U.S. government license for such positions, and Employer may decline to
proceed with an applicant on this basis alone.