Principal Engineer - Data Engineering Architect

Overview

Define and advance the data information strategy, information models, data sourcing standards, and data enablement roadmap across Enterprise Risk, Audit, and Finance Technology. This is a horizontal architecture role supporting multiple business and technology domains.

What you'll do

• Act as an advisor to leadership to develop or influence applications, network, information security, database, operating systems, or web technologies for highly complex business and technical needs across multiple groups.
• Lead the strategy and resolution of highly complex and unique challenges requiring in-depth evaluation across multiple areas or the enterprise, delivering long-term, large-scale solutions.
• Translate advanced technology experience and knowledge of business objectives into technical engineering solutions.
• Provide vision, direction and expertise to leadership on implementing innovative and significant business solutions.
• Maintain knowledge of industry best practices and new technologies and recommend innovations that enhance operations or provide a competitive advantage.
• Strategically engage with all levels of professionals and managers across the enterprise and serve as an expert advisor to leadership.
• Define and own the data information strategy and target-state information architecture across ERAFT.
• Develop current-state, target-state, and transition-state architectures covering information domains, data sourcing, information models, metadata, lineage, data products, semantic access, integration, and consumption.
• Define canonical information models and common business concepts reusable across Risk, Finance, Audit, regulatory reporting, analytics, and AI use cases.
• Establish standards and artifacts for conceptual and logical data models, taxonomies, ontologies, business glossaries, source-to-target mappings, data flows, data lineage views, and semantic relationships.
• Align ERAFT information architecture with enterprise data, metadata, privacy, security, retention, records-management, and regulatory standards.
• Produce architecture specifications, decision records, reference models, roadmaps, standards, and executive-level recommendations; develop POCs and demos to supplement architecture materials.
• Define reusable data sourcing strategies and identify authoritative systems of record, systems of reference, and trusted consumption sources for critical enterprise information.
• Establish criteria for selecting sourcing approaches (direct-source integration, data products, APIs, event streams, batch feeds, warehouses, lakes, data virtualization, federated queries, metadata-driven ingestion).
• Partner with source-system owners, data owners, and data stewards to assess data availability, ownership, quality, lineage, refresh frequency, historical coverage, access restrictions, and regulatory obligations.
• Reduce duplicate sourcing, unnecessary data movement, point-to-point integration, and inconsistent transformation logic.
• Define data contracts documenting schema, semantics, quality expectations, service levels, usage constraints, and change-management responsibilities.
• Develop and maintain enterprise and domain-level information models; standardize business concepts, definitions, relationships, taxonomies, and metrics across Risk, Finance, Audit, and related regulatory domains.
• Define semantic-layer patterns that separate business meaning from physical data structures and technology implementations.
• Define information-governance requirements for ERAFT data products, platforms, and solutions, and establish accountability for business terms, data domains, data products, and Critical Data Elements.
• Define data-quality expectations for completeness, accuracy, timeliness, validity, consistency, and uniqueness, and patterns for controls, reconciliation, validation, observability, and issue remediation.
• Define end-to-end lineage requirements from authoritative sources through to downstream consumers, and metadata requirements supporting discovery, reuse, impact analysis, auditability, explainability, and regulatory response.
• Define the data foundation required for dashboards, reports, analytics, advanced monitoring, and AI-enabled insights; define data-readiness criteria for AI use cases.
• Define information structures and metadata required for AI retrieval, grounding, context management, model inputs, evaluation, and human review.
• Partner with enterprise, application, platform, solution architects and engineering teams to translate information architecture into implementable solutions.
• Define patterns for APIs, event streaming, batch ingestion, ETL and ELT, files, data virtualization, federated queries, and metadata-driven connectors.
• Provide data-architecture input to platform selection, vendor assessment, and build-versus-buy decisions; guide modernization from fragmented legacy data flows.
• Act as a trusted information-architecture adviser to business, data, technology, architecture, risk, finance, audit, compliance, and control leaders.
• Present data and information architecture strategies, target states, trade-offs, risks, and roadmap recommendations to senior leaders and governance forums.
• Document major sourcing, modeling, semantic, integration, and platform decisions through architecture decision records; identify architecture risks, dependencies, gaps, exceptions, and remediation requirements.
• Mentor architects, data modelers, analysts, and engineers in information-architecture practices.

What you'll need

• 7+ years of Engineering experience, or equivalent demonstrated through work experience, training, military experience, or education.
• 7+ years of experience in data architecture, information architecture, enterprise architecture, information modeling, or data-platform design.
• 7+ years of experience supporting banking, financial services, finance, risk, audit, regulatory reporting, compliance, or control-related domains.

Nice to have

• Experience spanning multiple Risk, Finance, Audit, Treasury, Financial Crimes, Model Risk, regulatory, or enterprise data-management domains.
• Experience with metadata management, data catalogs, business glossaries, data lineage, data-quality platforms, data products, and data contracts.
• Experience with semantic layers, ontologies, knowledge graphs, or enterprise information models.
• Understanding of data mesh, data fabric, authoritative data sources, and contract-driven data distribution.
• Experience with APIs, Kafka or event streaming, ETL and ELT, data virtualization, data lakes, lakehouse platforms, and enterprise data warehouses.
• Experience with technologies such as Dremio, Databricks, Snowflake, Spark, Python, SQL, Neo4J, Protégé, object storage, and metadata or catalog platforms.
• Familiarity with graph technologies, vector databases, retrieval-augmented generation, enterprise large-language-model platforms, and AI orchestration frameworks.
• Experience defining governed data patterns for AI, including source traceability, context, lineage, human validation, explainability, and auditability.
• Familiarity with enterprise architecture frameworks such as TOGAF or equivalent.
• Relevant architecture, information-management, cloud, data-governance, or security certification.

Details

• Location: Bengaluru, India.
• Posting end date: 18 Oct 2026.