SARAS ANALYTICS
Forward Deployed Engineer
You'll build on top of Saras IQ Product to own the solution and the build for a client requirement — from the first requirements conversation and live customer demos to validated data, one trusted set of metrics, live dashboards, and clean data every morning — as the accountable owner of what gets built as a  repeatable product capability.

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. Whatever you build, you then become the advisor or implementer of that same capability in the product: you create repeatable solutions that expand what the product can do and maximize revenue impact. On that account you work alongside a Strategic Account Manager, who owns the client relationship and the commercial outcomes, and a Product Manager, who owns product onboarding, enablement, and roadmap, with you as a direct contributor to it. You own what gets built and whether it is right — requirements, software, data, metrics, dashboards, and data quality — with our AI-assisted tooling and our SME leads behind you for the hardest problems.

The Bar

Speed & 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. Speed matters because clients feel it — the shortest path from signed-off requirements to a live, trusted dashboard — but never at the cost of numbers the client can rely on. 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, you internally escalate the dependencies on time. The lanes are explicit: you own the solution and the build; the Account Manager owns the relationship and the commercial outcomes; the Product Manager owns product onboarding, enablement, and roadmap, with you as a direct contributor to it.

What You'll Own

  1. Own discovery and requirements. Join the sales handover to close open questions and call out risks early. Run discovery sessions directly with client stakeholders on the client's core use cases — sales and marketing, contribution margin, customer cohorts, product and inventory — co-design the solution with them, and write the business requirements and metric definitions using Saras' standard templates, reviewing them internally before client sign-off. Build the mock-ups that let clients react before anything is built, demo working builds live to the client at every milestone and take their feedback directly, write the test cases every requirement will be validated against, and track change requests so scope stays visible to you and the Account Manager.
  2. Own the data build. Set up integrations and access with our data-ingestion team, translate requirements into technical specifications, and build the staging, master, and presentation datasets with Saras' AI-assisted data-engineering tools, following our data-model conventions (dbt, BigQuery, Git). Apply PII masking, and keep the data dictionary and lineage documentation current. When a problem goes beyond your account — a complex architecture choice or a platform limit — take it to the senior data-engineering lead with a proposed workaround, and keep ownership of the outcome.
  3. Build on top of the product, end to end. You are not only modelling data: you build complete solutions on top of Saras IQ, and that takes software & data engineering & Analytics skills. Design and code the services, APIs, integrations, automation, and application logic around the data layer, and apply sound engineering practice — modular design, code review, automated tests, version control, and CI/CD. Write code that others can maintain and extend, because it may become part of the product.
  4. Turn what you build into product. Whatever you build, you become the advisor or implementer of that same capability in the product. Package it as a repeatable, configurable solution rather than a one-off, document it, and help other FDEs and accounts adopt it, so each engagement expands what the product can do and maximizes its revenue impact.
  5. Own the client's metric definitions. Configure one certified set of metrics and dimensions for each client in Saras IQ's semantic layer, so that every dashboard, self-serve question, and AI answer draws on the same definitions.
  6. Own validation before anything reaches the client. Reconcile every dataset against its source of truth, certify it, and run the internal sign-off against agreed criteria for sending work back. Set up the data-quality and completeness checks, validate against the requirement test cases and against a benchmark set of business questions the AI analyst must answer correctly, then walk the Account Manager through the build in a final review before it goes to the client.
  7. Own the consumption layer. Build dashboards natively in Saras IQ (and in Tableau, Luzmo, or Power BI where the contract specifies), drawing their metrics from the client's certified definitions. Build and maintain the business context that powers Saras IQ Analyst, our AI analyst; set it up and provision it for client users in Slack and their AI tools; and keep refining that context from what users ask it. The Account Manager leads the launch and adoption, and the Product Manager leads enablement.
  8. Own data quality every day. Run the daily data-quality cycle: confirm the overnight pipelines and checks have completed, resolve what they flag, escalate anything still open — or late upstream data — to the senior data-engineering lead, re-run and re-test what you fixed, and send the client a routine data-quality status (with any help you need from them), copying the Account Manager. Configure latency and dashboard alerts, and close every bug and change request with a root cause and a fix. When an issue breaches its SLA or stays open past a day, the Account Manager takes the client escalation and you drive the fix.
  9. Run your delivery hygiene. Keep your onboarding and project documentation, task tracker, and timesheets current, so anyone can see where your account stands.
  10. Partner across the lanes. Be the direct technical voice to customers and to the product teams, and contribute to the product roadmap. Support your Account Manager with the technical answers behind success criteria, client acceptance testing, and adoption, and demo what you built and walk client users through its logic yourself, while the Account Manager owns the relationship and the commercial conversations; work with the Product Manager and the product teams to turn what you hear from customers and learn from your builds into roadmap priorities; and work with our data-ingestion and data-engineering teams to unblock ingestion, data quality, and architecture issues.

What Winning Looks Like

First 90 days: you know your account's data and metric definitions in depth, you run the daily data-quality cycle reliably, and you've taken at least one deliverable from signed-off requirements to a validated, live dashboard on your own, demoing it to the client yourself.
Steady state: you deliver datasets and dashboards independently and on the committed date, your validation catches issues before clients ever do, your client's mornings start with clean data, your FDE Lead trusts what you ship without re-checking every number, your teammates enjoy working with you, what you've built is reusable and part of the product, you contribute to the roadmap, and you're ready to own a new account launch end to end.

Who Thrives Here

Any of these backgrounds can get you in the room. Across all of them, 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.
  • You've been a business or data analyst who wrote requirements and specifications, worked hands-on in SQL, and are ready to own the build as well as the brief.
  • You've been an analytics or data engineer who built layered data models — raw, staging, masters, presentation — and are ready to own the client conversation as well as the pipeline.
  • You've delivered data or BI projects in an analytics consulting or services firm, end to end, directly with clients.
  • You've been a software engineer who built backend or full-stack applications and data-heavy products, and are ready to take on analytics, data modelling, and the client conversation.
Whichever route you come from, you have:
  • A degree from a reputed engineering college.
  • Strong SQL and data-modelling fundamentals across the raw → staging → master → presentation layers.
  • Software engineering fundamentals: proficiency in Python (or a comparable language such as Java, TypeScript, or Go), data structures, and the ability to write clean, modular, tested, maintainable code.
  • Software design skills: you can design the architecture of an end-to-end solution — services, APIs, data flows, and integrations — weigh the trade-offs, and build it to be reusable and extensible.
  • Engineering practice: code review, unit and integration testing, CI/CD, and debugging in production.
  • Hands-on experience with at least one BI tool — Tableau, Power BI, or similar.
  • A foundational understanding of statistics — enough to know when a number is meaningful and when it isn't.
  • Working knowledge of dbt and BigQuery (or the ability to ramp up quickly), and everyday use of Git.
  • Comfort with AI-assisted development tools such as Cursor, Claude, or GitHub Copilot — we'll train you on Saras' own tooling.
  • The ability to turn an ambiguous client ask into clear, testable requirements and a technical specification.
  • Clear, confident client communication — you can own a delivery conversation without a go-between, run live product demos, and present to client and product stakeholders.
  • The ability to work with product teams and contribute to a roadmap: you can turn customer needs and what you've built into reusable product capabilities.
It's an even stronger fit if you also bring:
  • An engineering degree in Computer Science, AI, Data Analytics, or Data Science.
  • Ecommerce data fluency: you know how contribution margin, customer cohorts, attribution, and inventory are really calculated — and where the source data misleads.
  • Experience with a semantic or metrics layer, or with data-quality and observability frameworks.
  • Dashboards you've built that clients actually use, and the instinct for what makes one trustworthy.
  • Experience with orchestration tools such as Airflow.
  • Experience building APIs, integrations, or full-stack applications, and with cloud services (GCP preferred) and containerization.
  • You've presented analysis directly to a client or senior stakeholder, not just to your own team.

Location

Hyderabad. Standard working hours are noon to 9 PM IST.

Compensation & Benefits

Competitive salary with performance-based bonuses, comprehensive health insurance, relocation support, and ongoing professional development. Saras Analytics is an equal opportunity employer committed to building an inclusive workplace for all.
If you want to own what gets built, end to end, on top of our product, and see it land with the client, then make it part of the product, we want to talk to you.

Required Skills

Problem Solving DBT communication SQL Cloud Data Engineering Data Architecture and Design
View all job openings