Senior Staff Data Engineer

Meeru AI Inc

18h•Remote

About The Position

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. 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.

Requirements

10+ years in data engineering, including significant time at Staff, Principal or Lead level.
Owned data architecture for a whole system, not just individual pipelines.
Designed a canonical or common data model used by many teams.
Defined shared entities, hierarchies, dimensions and time periods so that many different sources fit into one model, and many consumers can rely on it.
Built systems so that new customers or sources are added by configuration, not by rewriting code.
Designed mapping frameworks or connector strategies that reuse work rather than fork it.
Expert SQL and dbt (or similar) modeling at scale.
Experience with window functions, large joins, incremental and change-data-capture (CDC) models, and performance tuning on large datasets.
Deep warehouse knowledge: Snowflake, BigQuery and/or PostgreSQL.
Understanding of partitioning, internals, and the cost-versus-performance trade-offs of each warehouse.
Production orchestration with Airflow, Dagster or Prefect.
Experience building idempotent and reproducible pipelines.
Familiarity with lineage, data contracts, data quality and schema evolution.
Experience setting 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.
Strong Python for pipelines, tooling and testing.
Experience leading and mentoring data engineers, ideally across distributed or offshore teams.
Clear communication with architects, backend and AI engineers, and product.
Willingness to work part of your day overlapping with US hours.

Nice To Haves

Financial and ERP source data.
Hands-on experience with NetSuite, SAP, Oracle or Workday data.
Understanding of how financial statements are built and reconciled (general ledger, AP, payroll, accruals, prepaids, equity).
Multi-cloud and customer-hosted deployments.
Experience running 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.
Experience building synthetic or "golden" test datasets.
Experience feeding ML or LLM systems from a curated data layer.
Understanding what AI teams need from clean, well-documented data.

Responsibilities

Build the machine-learning models that learn each customer's patterns.
Build LLM and agentic systems that produce grounded natural-language output.
Uphold the rigor that keeps output faithful.
Own significant pieces of the AI and intelligence layer end-to-end.
Build per-customer models without leaking signal.
Turn designs into robust production systems.
Measure quality rigorously.
Help turn user feedback into durable improvements.
Grow toward staff-level technical ownership.
Lead and mentor data engineers, ideally across distributed or offshore teams.
Run design reviews, write standards, and raise the bar without becoming the bottleneck.
Explain and defend architecture decisions in writing and in meetings.