Staff Data Engineer

Meeru AI Inc•Pleasanton, CA

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 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.
Experience owning complex, production pipelines end to end, including performance, reliability, and cost.
Expert SQL and data modeling skills.
Experience with complex, set-based transformations over a cloud warehouse (Snowflake, BigQuery, PostgreSQL or similar).
Experience designing models for others to build on.
Experience with dbt in production, including building and maintaining real dbt projects with incremental models.
Experience with production orchestration using Airflow or Dagster, designing safe DAGs for reruns and backfills.
Knowledge of warehouse internals and performance tuning for large datasets.
A bias toward correctness, treating data tests, reconciliation, and lineage as core job functions.
Ability to build idempotent and reproducible pipelines.
Solid Python skills for writing maintainable pipeline code, tests, and tooling.
Experience integrating with real-world sources with inconsistent data and changing schemas.
Comfort with collaboration and mentorship, including agreeing data contracts and reviewing peer work.

Nice To Haves

Experience with ERP and financial data (NetSuite, SAP, Oracle, Workday, general ledger, accounts payable, payroll, HRIS).
Experience with CDC and managed ingestion tools like Fivetran.
Experience working across multi-cloud environments (AWS, Azure, GCP), especially with pipelines running inside customer environments with strict tenant isolation.
Experience with a wider range of warehouses (Oracle ADW, Apache Pinot, MySQL).
FinTech or financial-services exposure, understanding the need for numbers that tie out to the cent.
Experience building reusable integration frameworks, connectors, or mappings as configuration.
Experience building golden or synthetic datasets for regression testing.

Responsibilities

Build and own major subsystems of the data foundation, including pipelines, connectors, and curated models.
Turn data architecture and standards into robust, production-grade systems.
Own end-to-end complex transformations over the warehouse.
Develop source integrations for new customer systems.
Implement reconciliation and lineage to prove curated models tie back to source.
Ensure pipelines perform well, are reliable, and cost-effective.
Write complex, set-based transformations over a cloud warehouse.
Design data models for other teams to build upon.
Build and maintain dbt projects, including efficient incremental models.
Design production orchestration DAGs with Airflow or Dagster that are safe to rerun and backfill.
Tune warehouse internals and query performance on large datasets.
Implement data tests, reconciliation against source, and lineage.
Build idempotent and reproducible pipelines.
Write pipeline code, tests, and tooling in Python.
Integrate real-world sources with inconsistent data and changing schemas.
Establish data contracts with backend and AI teams.
Review peers' work and mentor senior and mid-level engineers.