Senior Staff Data Engineer

Meeru AI is building an AI-native platform that transforms how finance and accounting teams operate. It connects to enterprise financial systems — ERPs, CRMs, billing platforms, HRIS — and applies machine learning to turn fragmented operational data into grounded, auditable intelligence for CFOs, controllers, and FP&A leaders. The platform is deployed as SaaS multi-tenant, SaaS single-tenant, and on-premises across AWS, Azure, and GCP. Customers are Fortune 500 finance teams requiring data isolation, auditability, and compliance.

• 10+ years in data engineering, including Staff, Principal, or Lead level.
• Ownership of entire data architecture systems.
• Design of canonical or common data models used by many teams.
• Ability to add new customers or sources by configuration, not code rewriting.
• Expert in SQL and dbt (or similar) modeling at scale, including window functions, large joins, CDC, and performance tuning.
• Deep knowledge of warehouses such as Snowflake, BigQuery, and/or PostgreSQL.
• Experience with production orchestration tools like Airflow, Dagster, or Prefect.
• Familiarity with lineage, data contracts, data quality, and schema evolution; experience with tools such as OpenLineage, dbt exposures, dbt tests, and Great Expectations.
• Commitment to correctness and traceability ensuring numbers always tie back to their source.
• Strong Python skills for pipelines, tooling, and testing.
• Experience leading and mentoring distributed or offshore data engineering teams.
• Clear communication skills in English, working with US-based colleagues.
• Willingness to work overlapping with US time zones.

Nice to have:

• Experience with financial and ERP source data (NetSuite, SAP, Oracle, Workday).
• Experience with multi-cloud and customer-hosted deployments including Docker and containers.
• Background in FinTech or financial services with exposure to SOC 2 or audit requirements.
• Experience building synthetic or golden test datasets.
• Experience feeding ML or LLM systems from curated data layers.

• Build machine-learning models that learn each customer's patterns.
• Develop LLM and agentic systems producing grounded natural-language output.
• Ensure all AI-generated outputs are traceable to verified data and uphold rigor.
• Own significant parts of the AI intelligence layer end-to-end.
• Work closely with Staff AI Engineer and evaluation engineer.
• Implement per-customer models maintaining strict anti-leakage discipline.
• Turn designs into robust production systems.
• Measure quality rigorously and improve systems based on user feedback.
• Collaborate closely with AI leadership based in the US.