Sr. Staff Data Engineer - IT

Actively Reviewing the Applications

Eaton

India, Maharashtra Full-Time

Posted 1 day ago • Apply by June 26, 2026

Job Description

What You’ll Do

We’re hiring a Senior Data Architect to lead the Finance Data Hub—designing the blueprint and standards for finance data assets across Snowflake, ADF, and Power BI. You’ll operationalize our data mesh with a medallion lakehouse, deliver robust semantic models for Commercial Analytics, Core Finanance (AP/AR/GL/FA), support a breadth of partnering data domains across Eaton, and embed governance and SOX‑ready controls so Finance can trust, scale, and automate decisions.

Own the end to end data architecture roadmap for our Snowflake centric Finance Data Hub and medallion/lakehouse patterns—aligning enterprise (bronze/silver) and domain (gold) layers for scale, reuse, and velocity.
Design and govern the semantic layer (enterprise curated datasets, star schemas, RLS) that delivers a single version of truth for analytics in Power BI; codify standards and deployment practices.
Assess application/data platform architecture choices: decide sourcing patterns, security rules (RBAC/RLS), privacy constraints, and data residency controls in partnership with platform/security teams.
Establish repeatable ingestion & transformation patterns with Azure Data Factory and Snowflake (orchestration, environments, naming, CI/CD), and champion DataOps guardrails.
Assess high level data architecture from context: translate objectives into conceptual entities (e.g., customer invoices, customer master, finance master data like site and accounts) and drive a fit for purpose target state.
Advance federated data governance and quality with domain owners and stewards—CDE identification, DQ rules, scorecards, lineage, and catalog practices that drive trust.
Raise our AI data readiness—ensure data products include the metadata, quality, lineage, and controls AI requires; align with emerging AI governance and risk processes.
Engineer for performance, reliability, and cost—optimize Snowflake warehouses, refresh/gateway health, and observability for >99% availability across the analytics estate.
Embed security and compliance by design—RBAC/RLS, encryption, least privilege, and cloud security controls across data stores, pipelines, and BI surface.
Coach and uplift talent—mentor architects, engineers, and stewards; cultivate reusable patterns, reference implementations, and strong “data as an asset” practices.
Operationalize CI/CD for data & BI—govern branching, releases, and deployment pipelines for Snowflake/Power BI; drive automated reconciliation and validation.
Partner across platform & analytics teams to harmonize ingestion/lakehouse with reporting and ML, accelerating domain roadmaps and cross domain reuse.
Co create test strategy and exit criteria with the Product Owner; define data/semantic validation and performance thresholds needed for release and sign off.
Joint design sign off: partner with DF&I techno functional leadership to review and sign off the detailed technical design and data model for FDH assets.

Qualifications

Bachelor’s in Computer Science, Data/Information Systems, Engineering, Mathematics, or related field (or equivalent experience).

8–10 years in data architecture/engineering with a record of shipping finance‑grade data assets and semantic models.

5+ years dimensional modeling and ELT/ETL for analytical workloads; 3+ years hands‑on with Snowflake, ADF, and Power BI at enterprise scale.

Skills

Architectures & Patterns: Data mesh (domain ownership, federated governance) and medallion lakehouse (bronze/silver/gold) applied to Finance use cases.
Snowflake (Finance focus): warehouse optimization, materialized views/search optimization, secure data sharing, and workload segregation for close windows.
ADF & DataOps: standardized pipelines, environment promotion, CI/CD options for data; defensible naming and metadata capture for auditability.
Semantic Layer & BI: Power BI deployment pipelines, DAX standards, Tabular Editor/DAX Studio usage, and scalable RLS patterns for sensitive finance hierarchies.
Modeling Standards: domain agnostic bronze/silver guidance and domain aligned finance gold (facts/dims for AP, AR, GL, FA, Intercompany); conformed dimensions and metric definitions.
Governance & MDM: CDE identification, glossary/lineage, DQ rules & scorecards; integration with finance hierarchies/MDM and stewardship councils.
Security/Compliance: RBAC, RLS, encryption and Azure security controls; awareness of SOX and data privacy impacts on finance reporting pipelines.
Testing & Readiness: automated reconciliation Snowflake→Power BI, SIT/UAT for KPI sign off, and performance testing for semantic models.
AI Data Readiness & Governance: AI risk checkpoints, data policy as code direction, and regulatory awareness embedded in asset lifecycle.
Business‑anchored architecture: connects high‑level finance outcomes (e.g., DSO reduction) to conceptual data models and target state architectures (invoices, customer master, finance master data).
Standards stewardship: demonstrated ability to apply and contribute to enterprise data mesh standards/playbooks, surfacing reusable patterns and guardrails."
Finance domain fluency communicates trade offs in plain language; aligns stakeholders around close timelines, reconciliations, and metric definitions.
Systems & lifecycle thinking connects ingestion→modeling→semantic→consumption with documentation and enablement that auditors and analyst’s trust.
Leadership & influence: mentors engineers/stewards; builds consensus across Finance, Platform, and Governance forums.
Outcome orientation: delivers measurable gains in reliability, quality, adoption, and cost within FDH.
Collaborative orchestration: not the expert in every domain, but consistently brings the right people together (DF&I product owner, Architecture Guild, IDM Finance BU team, platform/security, stewards) to move from intent → design → sign off → release.