# Senior Staff Data Engineer **Company:** [Meeru AI Inc](https://jobs.workable.com/companies/uiLW53dncYSvATpHSz2v6c.md) **Location:** Remote **Workplace:** remote [Apply for this job](https://jobs.workable.com/view/5166e5c0-face-401d-8398-c0dd47030072) ## Description About Meeru AI Meeru AI is building an AI-native platform that transforms how finance and accounting teams operate. We connect to enterprise financial systems — ERPs, CRMs, billing platforms, HRIS — and apply machine learning to turn fragmented operational data into grounded, auditable intelligence for CFOs, controllers, and FP&A leaders. We deploy on customer terms — **SaaS multi-tenant, SaaS single-tenant, and on-premises** — across **AWS, Azure, and GCP**. Our customers are Fortune 500 finance teams who require data isolation, auditability, and compliance. The Role We are looking for a **Senior AI Engineer** to build the AI and intelligence layer — and help uphold the discipline that keeps it honest. Our output sits adjacent to externally reported financials, so "sounds plausible" is not good enough: everything the AI produces must be grounded in, and traceable to, verified data. You'll build the machine-learning models that learn each customer's patterns, the LLM and agentic systems that produce grounded natural-language output, and help uphold the rigor that keeps that output faithful. You own significant pieces of the layer end-to-end, working closely with our Staff AI Engineer and evaluation engineer, and you build per-customer models without leaking the very signal they're meant to detect. This is a hands-on engineering role. You turn designs into robust production systems, measure quality rigorously, help turn user feedback into durable improvements, and grow toward staff-level technical ownership. ### Location & engagement - **Offshore** — Open to remote globally, working with our distributed engineering team. - **Flexible engagement** — open to **full-time, contract, or contract-to-hire**, whichever fits you and the engagement best. - **US partnership** — you partner closely with US-based AI leadership, with a few hours of daily time-zone overlap. ### What makes this role different - **Grounding is a hard requirement** — no fabricated or unsupported output, ever. Faithfulness is a gate, not a tuning goal. - **Clear separation of concerns** — ML surfaces and prioritizes; generated text only states what is supported by verified data or confirmed by a human. - **Per-customer intelligence** — models that learn each business's behavior, with strict anti-leakage discipline. - **Deploys in customer clouds** — including managed LLMs in-VPC, so customer data never leaves their environment. ## Requirements - **10+ years in data engineering, including significant time at Staff, Principal or Lead level.** You have owned data architecture for a whole system, not just individual pipelines. - **You have designed a canonical or common data model used by many teams.** You know how to define shared entities, hierarchies, dimensions and time periods so that many different sources fit into one model, and many consumers can rely on it. - **You build so that new customers or sources are added by configuration, not by rewriting code.** You have designed mapping frameworks or connector strategies that reuse work rather than fork it, and you can explain when to build versus configure. - **Expert SQL and dbt (or similar) modeling at scale.** Window functions, large joins, incremental and change-data-capture (CDC) models, and performance tuning on large datasets are everyday tools for you. - **Deep warehouse knowledge: Snowflake, BigQuery and/or PostgreSQL.** You understand partitioning, internals, and the cost-versus-performance trade-offs of each. - **Production orchestration with Airflow, Dagster or Prefect.** Your pipelines are idempotent and reproducible: the same inputs always produce the same outputs, and re-runs are safe. - **Lineage, data contracts, data quality and schema evolution are second nature.** You have set up lineage capture (e.g. OpenLineage, dbt exposures), quality tests (dbt tests, Great Expectations), and checks that stop a build when numbers don't reconcile. - **A firm commitment to correctness and traceability.** "Every number ties back to its source" is a standard you hold, even under deadline pressure. Our data sits next to companies' reported financials and must stand up to audit. - **Strong Python** for pipelines, tooling and testing. - **Experience leading and mentoring data engineers, ideally across distributed or offshore teams.** You run design reviews, write standards, and raise the bar without becoming the bottleneck. - **Clear communication with architects, backend and AI engineers, and product.** You can explain and defend architecture decisions in writing and in meetings, in English, with US-based colleagues. - **Willingness to work part of your day overlapping with US hours.** Our leadership is US-based. ### Nice-to-haves - **Financial and ERP source data.** Hands-on experience with NetSuite, SAP, Oracle or Workday data, and an understanding of how financial statements are built and reconciled (general ledger, AP, payroll, accruals, prepaids, equity). - **Multi-cloud and customer-hosted deployments.** You have run data workloads across AWS, Azure and GCP, including inside a customer's own cloud with Docker/containers, strict tenant isolation and no data leaving their environment. - **FinTech or financial-services background**, including exposure to SOC 2 or audit expectations for data. - **Synthetic or "golden" test datasets.** You have built realistic test data so teams could develop and regression-test before real customer data was available. - **Feeding ML or LLM systems from a curated data layer.** You understand what AI teams need from clean, well-documented data.