Job 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 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. • 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 run. • Expert 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 on. • dbt in production. You've built and maintained real dbt projects, including incremental models that refresh efficiently instead of rebuilding everything. • Production orchestration with Airflow or Dagster. You design DAGs that are safe to rerun and backfill. • Warehouse internals and performance tuning. You know why a query is slow or expensive on large datasets and how to fix it. • A 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 reproducible. • Solid Python. Enough to write pipeline code, tests and tooling that others can maintain. • Comfort with messy source systems. You've integrated real-world sources with inconsistent data and changing schemas, and kept downstream models stable. • Collaboration and mentorship. You agree data contracts with backend and AI teams, review peers' work and mentor senior and mid-level engineers. Nice 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 HRIS. • CDC and managed ingestion. Change-data-capture pipelines or tools like Fivetran. • Multi-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 leaving. • A wider warehouse range. Oracle ADW, Apache Pinot or MySQL alongside the main warehouses. • FinTech or financial-services exposure. You understand why finance teams need numbers that tie out to the cent. • Reusable integration frameworks. You've built connectors or mappings as configuration on a shared framework, so the next source is faster to onboard. • Test and synthetic data. You've built golden or synthetic datasets for regression testing. • Interest in architectural ownership. This role has a clear path toward Senior Staff.

Requirements

Must-haves

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 run.
Expert 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 on.
dbt in production. You've built and maintained real dbt projects, including incremental models that refresh efficiently instead of rebuilding everything.
Production orchestration with Airflow or Dagster. You design DAGs that are safe to rerun and backfill.
Warehouse internals and performance tuning. You know why a query is slow or expensive on large datasets and how to fix it.
A 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 reproducible.
Solid Python. Enough to write pipeline code, tests and tooling that others can maintain.
Comfort with messy source systems. You've integrated real-world sources with inconsistent data and changing schemas, and kept downstream models stable.
Collaboration and mentorship. You agree data contracts with backend and AI teams, review peers' work and mentor senior and mid-level engineers.

Nice-to-haves

ERP and financial data. Experience with NetSuite, SAP, Oracle or Workday, or with finance data such as general ledger, accounts payable, payroll and HRIS.
CDC and managed ingestion. Change-data-capture pipelines or tools like Fivetran.
Multi-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 leaving.
A wider warehouse range. Oracle ADW, Apache Pinot or MySQL alongside the main warehouses.
FinTech or financial-services exposure. You understand why finance teams need numbers that tie out to the cent.
Reusable integration frameworks. You've built connectors or mappings as configuration on a shared framework, so the next source is faster to onboard.
Test and synthetic data. You've built golden or synthetic datasets for regression testing.
Interest in architectural ownership. This role has a clear path toward Senior Staff.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningawsazuregcpsqlpython

Platform: workable
Effort: medium