Sr. Forward Deployed Engineer (FDE) - Retail

Databricks · Northeast - United States; Southeast - United States

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Role at a Glance

A senior hands-on engineering role where you own both the architecture and the code for enterprise retail clients on the Databricks platform. You'll embed inside customer organizations, drive data and AI system deployments end-to-end, and contribute reusable assets back to the broader practice. Requires 6+ years of experience, deep Apache Spark expertise, and multi-cloud fluency. Base salary: $182,000–$250,208. Expect roughly 20% customer travel.

What This Role Actually Involves

Databricks Forward Deployed Engineers are builders, not advisors. In this retail-focused senior role, you'll spend your time inside enterprise customers' environments — diagnosing data architecture challenges, designing systems that address them, and then shipping those systems yourself. You own engagements from initial scoping through production deployment, operate across stakeholder levels from individual contributors to executives, and serve as the technical anchor between the customer and Databricks' internal engineering and product teams. The role is billable and delivery-oriented; stakeholder management without a working system at the end is not the job.

About Databricks

Databricks originated as an academic project at UC Berkeley, where its founders created Apache Spark, Delta Lake, and MLflow before spinning out to commercialize them. Those open-source tools now underpin the data infrastructure of more than 10,000 organizations globally, including the majority of the Fortune 500. The company's commercial platform unifies data engineering, analytics, and AI workloads into a single environment. Headquartered in San Francisco with offices around the world, Databricks occupies an unusual position: engineering DNA rooted in academic research paired with enterprise customer scale.

What You'll Be Doing

  • Design and deliver production-grade data architectures for retail enterprise customers — spanning ingestion pipelines, ML/AI model integration, and end-user-facing application layers
  • Lead all technical architecture and design decisions for customer engagements, holding the bar on scalability, security, and Databricks platform alignment
  • Embed with customer teams across seniority levels — from individual contributors to the C-suite — to surface challenges and translate them into shipped solutions
  • Collaborate with engagement managers to define technical scope, set delivery milestones, and track measurable outcomes throughout the project lifecycle
  • Relay structured product and implementation feedback to Databricks engineering and support teams; help triage and resolve engagement-specific issues
  • Build reusable accelerators, reference architectures, and documented frameworks that extend your impact across multiple customer accounts and inform the product roadmap
  • Travel to customer sites approximately 20% of the time to support hands-on delivery and relationship depth

Must-Have Qualifications

  • 6 or more years of professional experience in data engineering, analytics platforms, or software engineering
  • Production-level coding ability in Python or Scala; JavaScript and/or TypeScript as a working complement
  • Hands-on experience across at least two of AWS, Azure, and GCP, with deep expertise in one platform
  • Advanced command of Apache Spark including familiarity with distributed execution internals — not just the API surface
  • Proven ability to design and ship end-to-end data systems that combine pipelines, ML models, and application layers in production
  • Track record managing technical project delivery: scope definition, timeline ownership, stakeholder communication, and measurable outcomes
  • Prior experience within enterprise client environments, including navigating complex cross-functional stakeholder dynamics
  • Willingness to travel to customer locations approximately 20% of the time

Helpful but Not Required

  • Active Databricks platform certification
  • Hands-on MLOps experience, including MLflow for experiment tracking and model lifecycle management
  • CI/CD pipeline experience applied specifically to production data deployments
  • Domain knowledge or prior project work in the retail industry vertical

Skills Breakdown: Required vs. Nice to Have

The non-negotiable technical core is Apache Spark at depth — the job description explicitly names runtime internals, not just API usage. Python or Scala is the expected primary language; JavaScript and TypeScript appear as supporting tools rather than primary requirements. Cloud breadth is a firm gate: two platforms minimum with one at expert level, so single-cloud specialists need to close a gap before applying. CI/CD and MLOps are listed as requirements but phrased with softer language ('familiarity,' 'working knowledge'), suggesting they function more as differentiators than hard disqualifiers. Databricks Certification is the last item on the list and reads as a strong-plus credential. Given the architecture-ownership mandate and six-year experience floor, this role operates at staff-engineer scope even without that title.

Salary and Compensation

Databricks has published an identical base salary band across all four of its US pay zones for this role: $182,000 to $250,208 per year. The consistent range regardless of geography is notable — it suggests the company has chosen not to apply cost-of-living discounts by region. The posting states total compensation may also include an annual performance bonus and equity grants, though the structure and size of those components are not specified in the job description. Candidates should ask directly about equity vesting schedules and bonus targets during the interview process.

Location and Work Arrangement

This role is open to candidates based in the US Northeast or Southeast regions. A formal remote, hybrid, or on-site classification is not specified in the posting. The 20% customer travel requirement — roughly one day per week averaged over time — is an explicit part of the role and should factor into any location decision. No relocation assistance or visa sponsorship details appear in the job description; candidates with specific needs on either front should raise them directly with Databricks' recruiting team.

Frequently asked questions

How much travel does this role require?

The job description specifies approximately 20% customer travel, which typically averages to around one day per week. Actual cadence will vary by engagement and customer location within the Northeast and Southeast US.

What is the base salary range for this position?

Databricks has published a base salary range of $182,000 to $250,208 USD, applied uniformly across all four of its US pay zones. Total compensation may also include annual performance bonuses and equity grants, though specific terms are not detailed in the posting.

Is Databricks Certification required to apply?

Databricks Certification is listed among the qualifications but appears at the end of the requirements, positioned alongside traits like curiosity and adaptability. This framing suggests it is a differentiating credential — valued and worth pursuing — rather than a hard gate that would disqualify an otherwise strong candidate.

Do I need expertise in all three cloud platforms — AWS, Azure, and GCP?

No. The posting requires working knowledge of at least two of the three major cloud ecosystems, plus demonstrated expertise in at least one. Candidates with depth in one platform and solid hands-on experience in a second would meet this bar.

Is this a remote, hybrid, or on-site position?

The posting does not specify a formal work model. Given the explicit 20% customer travel requirement, some degree of in-person presence is built into the role. Candidates should ask Databricks' recruiting team directly about the expected day-to-day base arrangement.

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