Req: Data Architect with hands on exp in AWS Bedrock, ETL, SQL, Informatica
Location: Remote
Key Skills Required
Must-have (prioritize candidates with depth in these areas):
Priority Skills
Critical Amazon Bedrock model selection, prompt design, Knowledge Bases, retrieval-augmented generation (RAG), guardrails, and production inference patterns
Critical AWS platform CI/CD (CodePipeline / GitHub Actions on AWS), Lambda, Step Functions, OpenSearch (vector search), Neptune or graph storage, Secrets Manager, S3 artifact pipelines, IAM, and cost/scale design
Required SQL Server at scale stored procedure dependency tracing, cross-database schema reconciliation, RLS / SESSION_CONTEXT
Required Dispositioning & triage turning automated outputs (schema drift, source-only tables, static-analysis findings) into auditable, dependency-backed decisions
Required ID de-collision analysis overlapping ID ranges, true collisions vs. co-mingled data, table-level resolution
Required ETL / batch consolidation Informatica (or equivalent), migration script quality, tenant-scoped batch jobs
Required LLM output validation guardian / evaluation patterns, golden-set regression, confidence thresholds
Required Code literacy (.NET, Java, Angular, Python); static analysis & SARIF; financial-services consolidation experience
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Role Summary
Senior practitioner who combines deterministic data engineering with Bedrock-powered AI reasoning to accelerate a large-scale multi-tenant consolidation. You turn automated signals schema drift, source-only tables, ID collisions, and static-analysis findings into defensible disposition decisions grounded in dependency chains (stored procedures, application code, ETL, batch jobs). You also productionize the toolchain on AWS: RAG through CI/CD, knowledge capture, and a guardian pattern that validates every model output before it reaches reviewers or migration teams.
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Core Responsibilities
1. Dispositioning Reasoning
Adjudicate schema drift, source-only tables, and migration-priority findings using SP app ETL dependency evidence.
Apply deterministic rules where the answer is deterministic; invoke Bedrock RAG where genuine judgment is required.
Produce auditable dispositions with confidence levels and blast-radius assessment; feed validated decisions back into the knowledge base.
2. ID De-Collision Reasoning
Investigate overlapping ID ranges; distinguish true collisions, co-mingled data, and namespace overlap without semantic conflict.
Define table-level resolutions (re-keying, offset mapping, surrogate keys, RLS-scoped acceptance) with end-to-end traceability through SPs, apps, and ETL.
3. Data Migration & Batch Consolidation
Review migration script quality (idempotency, ordering, rollback, tenant scoping).
Consolidate Informatica / batch jobs for tenant-specific routing; align with comment-out vs. refactor vs. data-layer-only strategies.
Support cutover sequencing, reconciliation, and post-migration validation.
4. Productionizing the Toolchain on AWS
Integrate the Bedrock RAG disposition assistant into CI/CD (retrieval over dependency graphs, schema diffs, SME feedback, prior dispositions).
Implement a guardian / evaluation pattern: grounding checks, structured-output validation, confidence thresholds, escalation for high-blast-radius decisions, golden-set regression.
Operationalize SME knowledge capture, observability, and versioning for prompts, indexes, and disposition rules.
5. Toolchain Assessment
Outside-in review of the code-graph generator (resolution accuracy, build vs. leverage, polyglot coverage).
Recommend AWS scaling paths: Bedrock Knowledge Bases, OpenSearch vectors, Neptune, serverless batch, SARIF/report pipelines.
Deliver a prioritized roadmap with measurable coverage improvement (EAC and disposition throughput).
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Qualifications
Required
8+ years in data engineering, platform migration, or enterprise modernization (financial services preferred).
Production experience with Amazon Bedrock and core AWS services (not exploratory POCs).
Large-scale SQL Server dependency analysis and schema reconciliation.
Hands-on ETL/batch consolidation (Informatica or equivalent).
CI/CD integration and LLM output validation in production workflows.
Preferred
Multi-tenant consolidation, tenant discriminators, feature entitlements.
Code graph / static analysis (Roslyn, OpenRewrite, CodeQL, custom analyzers).
Guardian patterns for LLM outputs; Python/PowerShell report pipeline automation.
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Engagement & Success Metrics
Phase Focus
Assess Toolchain review; disposition backlog; ID collision inventory
Establish Disposition standards; Bedrock RAG + guardian pattern; CI/CD skeleton
Execute Domain dispositioning; migration/batch review; ID resolution
Operationalize Knowledge loops, metrics, program handoff
Success: 90% SME acceptance on first review; guardian catches 95% of ungrounded model outputs; zero post-cutover ID collision defects in pilot domains; documented AWS toolchain roadmap with coverage targets.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: 91174879
- Position Id: 9072049
- Posted 1 hour ago
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