Forward Deployed Data Engineer

Job Description

We're looking for a Forward Deployed Data Engineer to join our team and embed directly with one of our clients, and own the data and automation infrastructure that powers their day-to-day operations.

This is a hybrid role by design. Part of it is the data engineering foundation: dbt models in BigQuery, event-driven pipelines on GCP (Pub/Sub, Cloud Run, Dataflow), infrastructure as code with Terraform, orchestration, and the testing and CI/CD practices that keep all of it reliable. Part of it is analytics engineering closer to the surface: reverse ETL into operational tools, dashboards, and bringing structure to messy data. And part of it is forward-deployed product work: sitting with client stakeholders (some technical, most domain experts), understanding what they actually need, and shipping bespoke automations end-to-end. You'll move fluidly between designing pipelines, writing SQL transformations, integrating with third-party APIs, and explaining to a non-technical user why their dashboard is showing what it's showing.

You'll work alongside Beyond Data's team: a Principal, a Senior Backend/AI Engineer, a Senior Data Architect, a Senior Infrastructure Architect, and another Senior Analytics Engineer. We're a small, senior-heavy team and we expect you to bring the seniority to propose ideas, make technical decisions, and own what you build.

The ideal candidate is comfortable in three modes: heads-down designing and building robust pipelines, hands-on debugging production data quality issues, and heads-up in a client call translating "we need to know which clients are slipping" into a concrete data model, transformation, and dashboard. You ship reliably, you ask good questions, and you're not precious about the layer of the stack you work in.

This is not a pure data, analytics, or engineering role. We're building not only data pipelines and dashboards, but also the connective tissue of the business operations--and we're doing it close to the people who use it all. If that sounds like the job you want, this is for you.

Responsibilities

• Build and maintain dbt transformation pipelines in BigQuery that power client-facing dashboards and operational automations
• Support and extend batch and event-driven data architecture built on Google Cloud Services (Pub/Sub, Cloud Run, Data flow etc)
• Manage infrastructure as code using Terraform
• Design schemas for previously unstructured data and migrate document-style records into structured form
• Build and maintain integrations with external systems, including reverse ETL flows from BigQuery into operational tools
• Ship bespoke automations end-to-end—from "this manual process is killing us" to a working system in production.
• Own a backlog of feature requests and data-quality work spanning multiple internal data products; triage, scope, and ship
• Build and refine dashboards and analytics tools based on stakeholder requirements
• Gather requirements directly from client stakeholders—technical leads, account managers, and domain experts—and translate them into technical solutions
• Mentor and provide technical direction to 1-2 engineers, with the team expected to grow over time
• Communicate progress, tradeoffs, and technical concepts to non-technical audiences
• Document data models, transformations, and integration architectures

Requirements

Need-to-have's

• Senior-level analytics engineering experience (5+ years)
• Strong expertise with dbt and modern analytics engineering practices
• Advanced SQL and experience with cloud data warehouses (BigQuery preferred, or Snowflake/Redshift)
• Solid Python skills—comfortable building integrations, ETL/reverse ETL, and small services (expert-level not required, but you should be able to ship production Python independently)
• Experience with GCP (our current cloud platform)
• Solid understanding of data engineering architectures and the ability to reason through trade-offs and justify technical decisions objectively
• Strong grounding in software engineering best practices - version control hygiene, code review, modular design, and writing well-tested code with appropriate coverage
• Familiarity with building and maintaining workflow orchestrators (Airflow, Dagster, or similar)
• Exposure to DevOps practices; comfortable setting up and debugging CI/CD pipelines when needed
• Proven track record of shipping features end-to-end, from stakeholder conversation to deployed system
• Experience building integrations with third-party APIs
• Comfortable working directly with clients and translating their needs into technical solutions—including non-technical stakeholders
• Strong sense of ownership and autonomy
• Clear written and verbal communication

Nice-to-have's

• Experience with reverse ETL tools and CRM/operational tool integrations
• Exposure to Kubernetes cluster management
• Experience working with unstructured or semi-structured data and bringing structure to messy datasets
• LLM integration experience (using LLMs for data extraction, classification, or enrichment)
• Experience building and deploying dashboards (Looker, Metabase, Hex, Superset, or similar)
• Experience with product analytics platforms (Amplitude, Segment, Mixpanel)
• Startup or consulting background
• Experience working in distributed/remote teams