Forward Deployed Data Engineer
Hiring immediately— we're actively interviewing and looking to fill this role asap.
Job Description
We're looking for a Forward Deployed Data Engineer to take ownership of the data, analytics, and automation function for one of our clients, leading the initiatives that power their day-to-day operations and setting the technical direction for where that work goes next.
You'll inherit a live, established set of data initiatives and become the person they run through - being the directly responsible owner for the client's data function and their primary point of contact for anything data, analytics, or automation. You'll be trusted to set the roadmap, make the technical calls, and grow the function and the team behind it, over time.
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), 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 data products and 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 lead this work alongside and set technical direction with Beyond Data's team: a Senior Backend/AI Engineer, a Senior Data Architect, a Senior Infrastructure Architect, and a Senior Analytics Engineer. We're a small, senior-heavy team, and we're looking for someone who can own the data initiatives outright: propose the ideas, make the decisions, and be accountable for what gets built.
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 of our 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, and it's not an execution-only one. You're taking over the connective tissue of the client's business operations - owning it, leading it, and doing it close to the people who use it all. To quote Steve Jobs: “The best managers are the people who are the best individual contributors who don’t want to be managers, but know that them being manager is the only way the job will get done right”. If that sounds like you, we want to meet you.
Responsibilities
Forward Deployed Responsibilities
- Own the data, analytics, and automation initiatives for the client end-to-end strategy, roadmap, and delivery
- Serve as the directly responsible owner and primary point of contact for the client on anything data-related
- Own a backlog of feature requests and data-quality work spanning multiple internal data products; set priorities, triage, scope, and ship
- Set technical direction, make architecture and tooling decisions, and be accountable for the trade-offs
- Provide technical direction to and mentor engineers on the team, and grow the team as the function scales
- Gather requirements directly from client stakeholders, technical leads, account managers, and domain experts and translate them into technical solutions
- Communicate progress, tradeoffs, and technical concepts to non-technical audiences
Individual Contributor 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, Dataflow, etc.)
- Manage infrastructure as code using Terraform
- 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
- Build and refine dashboards and analytics tools based on stakeholder requirements
- Document data models, transformations, and integration architectures
Requirements
Need-to-have's
- Senior-level analytics engineering experience (5+ years), with a track record of owning a data function or initiative - not just executing against someone else's roadmap
- Demonstrated ability to set technical direction, make decisions autonomously, and be the person accountable for outcomes
- 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
- Experience building and maintaining workflow orchestration using Dagster
- 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 mentoring and/or leading a small data/analytics team
- Experience with reverse ETL tools and CRM/operational tool integrations
- 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 (Studio), Omni, or similar)
- Experience with product analytics platforms (Amplitude, Segment, Mixpanel)
- Startup or consulting background
- Experience working in distributed/remote teams
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