Staff Data Engineer

About Meeru AIMeeru 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.About Meeru AIMeeru 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
Staff Data Engineer
to build and own the major subsystems of the data foundation — the pipelines, connectors, and curated models that feed the platform's analytics and AI layers. You take the data architecture and standards set by the data lead and turn them into robust, production-grade systems that don't break and don't lie about the numbers.You own significant pieces of the platform end to end complex transformations over the warehouse, source integrations for new customer systems, and the reconciliation and lineage that prove the curated model ties back to source. This is a hands‑on, high‑ownership engineering role with a clear path toward Senior Staff.Requirements
7+ years in data engineering. You've owned complex, production pipelines end to end, including how they perform, how reliable they are and what they cost to runExpert SQL and data modeling. You write complex, set-based transformations over a cloud warehouse (Snowflake, BigQuery, PostgreSQL or similar) and design models other teams build ondbt in production. You've built and maintained real dbt projects, including incremental models that refresh efficiently instead of rebuilding everythingProduction orchestration with Airflow or Dagster. You design DAGs that are safe to rerun and backfillWarehouse internals and performance tuning. You know why a query is slow or expensive on large datasets and how to fix itA bias toward correctness. You treat data tests, reconciliation against source and lineage as core to the job, and you build pipelines that are idempotent and reproducibleSolid Python. Enough to write pipeline code, tests and tooling that others can maintainComfort with messy source systems. You've integrated real-world sources with inconsistent data and changing schemas, and kept downstream models stableCollaboration and mentorship. You agree data contracts with backend and AI teams, review peers' work and mentor senior and mid-level engineersNice to have
ERP and financial data. Experience with NetSuite, SAP, Oracle or Workday, or with finance data such as general ledger, accounts payable, payroll and HRISCDC and managed ingestion. Change-data-capture pipelines or tools like FivetranMulti-cloud and customer-hosted deployments. Work across AWS, Azure and GCP, ideally where pipelines run inside customer environments with strict tenant isolation and no data leavingA wider warehouse range. Oracle ADW, Apache Pinot or MySQL alongside the main warehousesFinTech or financial-services exposure. You understand why finance teams need numbers that tie out to the centReusable integration frameworks. You've built connectors or mappings as configuration on a shared framework, so the next source is faster to onboardTest and synthetic data. You've built golden or synthetic datasets for regression testingInterest in architectural ownership. This role has a clear path toward Senior Staff
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