Forward Deployed Engineer

Role Overview:

You'll build on top of Saras IQ Product to own the solution and the build for a client requirement.

About Saras Analytics:

Saras Analytics is an AI and data analytics firm built for ecommerce and omnichannel brands. We help fast-growing SMBs and mid-market brands turn disconnected, messy data into decisions, covering ingestion, transformation, and consumption, backed by our own products (Daton and Saras IQ) as the data infrastructure in every engagement.

Why Now:

Saras is profitable and bootstrapped. We run flat: talent and good ideas move fast here regardless of tenure or title. AI-assisted tooling now lets one engineer take a client from the first requirements conversation to a live, trusted dashboard without waiting on a queue of hand-offs. The Forward Deployed Engineer role is built around that: one owner, end to end, with the tooling to make it fast. Because you build on top of our products rather than around them, what you create for one client requirement becomes capability that every client can use. An FDE joining now helps define what that role looks like at its best.

The Role:

You'll own the solution and the build for one client account, reporting to an FDE Lead. You build on top of our product, Saras IQ, extending it with software you design and code. You are responsible for interacting directly with the customer, running discovery, demoing what you build, and presenting it to client stakeholders, as well as with the product teams, and you contribute to the product roadmap.

The Bar:

Speed and delivery quality are the top priorities for this role: what you ship is correct, validated against the source of truth before the client sees it, and there on the date you committed to. You build to product standard: what you write is well designed, tested, version-controlled, and reusable, because it is part of the product. You surface risks before they block delivery, and you close every issue with a root cause and a fix.

What You'll Own:

• Own discovery and requirements: join the sales handover, run discovery sessions with client stakeholders, co-design the solution, write business requirements and metric definitions, build mock-ups, demo working builds, and track change requests.
• Own the data build: set up integrations and access, translate requirements into technical specifications, build staging, master, and presentation datasets following dbt, BigQuery, and Git conventions, apply PII masking, and keep documentation current.
• Build on top of the product, end to end: design and code services, APIs, integrations, automation, and application logic around the data layer, applying modular design, code review, automated tests, version control, and CI/CD.
• Turn what you build into product: package builds as repeatable, configurable solutions, document them, and help other FDEs and accounts adopt them.
• Own the client's metric definitions: configure one certified set of metrics and dimensions for each client in Saras IQ's semantic layer.
• Own validation before anything reaches the client: reconcile datasets against sources of truth, run data-quality and completeness checks, and walk the Account Manager through the build before client delivery.
• Own the consumption layer: build dashboards natively in Saras IQ (and in Tableau, Luzmo, or Power BI where the contract specifies), and build and maintain business context for Saras IQ Analyst.
• Own data quality every day: run the daily data-quality cycle, resolve flagged issues, escalate open items, re-test fixes, send routine data-quality status to the client, and close bugs with a root cause and a fix.
• Run your delivery hygiene: keep onboarding and project documentation, task tracker, and timesheets current.
• Partner across the lanes: serve as the direct technical voice to customers and product teams, support the Account Manager, and work with data-ingestion and data-engineering teams.

What Winning Looks Like:

First 90 days: you know your account's data and metric definitions in depth, run the daily data-quality cycle reliably, and take at least one deliverable from signed-off requirements to a validated, live dashboard on your own.

Steady state: you deliver datasets and dashboards independently and on the committed date, your validation catches issues before clients do, what you build is reusable and part of the product, and you contribute to the roadmap.

Who Thrives Here:

You bring 3+ years (typically 3-6) of hands-on data and software work, some of it building in SQL and code, and some of it facing clients, including demos.

• Business or data analyst who wrote requirements and specifications, hands-on in SQL.
• Analytics or data engineer who built layered data models (raw, staging, masters, presentation).
• Data or BI projects delivered in an analytics consulting or services firm, end to end, directly with clients.
• Software engineer who built backend or full-stack applications and data-heavy products.

Required foundations:

• A degree from a reputed engineering college.
• Strong SQL and data-modelling fundamentals across raw, staging, master, and presentation layers.
• Software engineering fundamentals: Python (or Java, TypeScript, or Go), data structures, and clean, modular, tested code.
• Software design skills: designing end-to-end architecture including services, APIs, data flows, and integrations.
• Engineering practice: code review, unit and integration testing, CI/CD, and production debugging.
• Hands-on experience with at least one BI tool such as Tableau or Power BI.
• A foundational understanding of statistics.
• Working knowledge of dbt and BigQuery, and everyday use of Git.
• Comfort with AI-assisted development tools such as Cursor, Claude, or GitHub Copilot.
• Ability to turn ambiguous client asks into clear, testable requirements and technical specifications.
• Clear, confident client communication and live product demos.
• Ability to work with product teams and contribute to a roadmap.

Stronger fit:

• An engineering degree in Computer Science, AI, Data Analytics, or Data Science.
• Ecommerce data fluency: contribution margin, customer cohorts, attribution, and inventory.
• Experience with a semantic or metrics layer, or data-quality and observability frameworks.
• Dashboards clients actually use.
• Experience with orchestration tools such as Airflow.
• Experience building APIs, integrations, or full-stack applications, and with cloud services (GCP preferred) and containerization.
• Experience presenting analysis directly to a client or senior stakeholder.

Compensation and Benefits:

Competitive salary with performance-based bonuses, comprehensive health insurance, relocation support, and ongoing professional development. Saras Analytics is an equal opportunity employer.