Senior Data Engineer, Consultant

DiligenceVault

Senior data engineering consultant designing PostgreSQL migration, canonical data, residency, governance, and AI-native architecture. Advising DiligenceVault’s investment due diligence SaaS platform through workshops and strategic deliverables.

Posted 8/5/2026contractRemote • 🇮🇳 IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in designing and architecting data platforms, particularly with PostgreSQL and its extensions, while ensuring compliance with data governance standards. Capable of producing strategic documentation and providing architectural guidance to leadership and senior architects.

Highest-signal resume keywords

PostgreSQL ArchitectureData Governance FrameworkCanonical Data Model DesignAI-Native Data EngineeringData Platform Migration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Platform ArchitectureRelational DatabasesETL/ELTDimensional ModelingPerformance TuningData ClassificationEntity ResolutionReplication TopologiesQuery OptimizationData Residency Compliance

Soft Skills

Exceptional CommunicationTeaching AbilityStrategic Thinking

Tools & Technologies

PostgreSQL ExtensionsPython/CeleryElasticsearchAzure Cloud ServicesDbtAirflowSnowflakeDatabricks

Industry Keywords

Financial ServicesInvestment ManagementDue DiligenceMulti-Tenant SaaS

Tech Stack

Tools & technologies

AirflowAzureCloudElasticSearchETL.NETPostGISPostgresPythonSQL

About the role

Key responsibilities & impact

Educate leadership and senior architects on traditional and AI-native data engineering concepts
Assess the current data infrastructure end to end, map data flows, identify gaps and technical debt, and produce a current-state/target-state assessment and prioritized roadmap
Design the target data platform architecture across ingestion, transformation, storage, serving, and observability
Architect the PostgreSQL migration and multi-workload environment, including pgvector, analytical workloads, transactional queries, pooling, replicas, partitioning, cutover, validation, query translation, and benchmarking
Design a canonical data layer for entity resolution, schema alignment, conflict resolution, temporal alignment, versioning, and auditability
Design data-residency architecture for a multi-tenant, data-sharing platform across regions and jurisdictions
Design the data governance framework covering access control, classification, lineage, retention, consent, quality accountability, and security controls
Define and prioritize data-platform use cases including cross-source intelligence, behavioral insights, enrichment, semantic search, compliance detection, and analytics/reporting
Produce architecture decision records, data-flow diagrams, tool evaluation guides, migration runbooks, and training decks
Provide ongoing architecture reviews, design consultations, and progress check-ins during execution
Deliver knowledge transfer, architectural guidance, strategic documents, and reference materials rather than primarily writing production code

Requirements

What you’ll need

8–10+ years building data platforms across heterogeneous sources at meaningful scale
Deep expertise in relational databases, specifically PostgreSQL
Hands-on experience with PostgreSQL extensions such as pgvector, Citus, PostGIS, or similar
Experience with replication topologies, partitioning, and performance tuning
Experience migrating from SQL Server to PostgreSQL strongly preferred
Experience designing canonical data models across disparate sources, including entity resolution, master data management, and conflict resolution at scale
Experience architecting multi-region or data-residency-compliant systems, ideally in a multi-tenant SaaS context
Strong understanding of data governance, including access control, data classification, lineage, retention policies, and regulatory compliance
Knowledge of dimensional modeling, ETL/ELT, CDC, orchestration, and query optimization
Active, informed engagement with AI-native approaches including ML-driven quality, semantic matching, embedding pipelines, and LLM-assisted development
Ability to design multi-layer platforms and make defensible technology choices considering scale, cost, team size, and maintainability
Exceptional communication and teaching ability with senior architects and leadership
Experience defining data use cases tied to business outcomes
Availability for approximately 4–5 hours per day, 20–25 hours per week, with overlap during US working hours (6:00 PM–11:00 PM IST)
Strong-to-have experience with Python/Celery, Elasticsearch, Azure cloud services, .NET APIs, Kestra or similar orchestration
Familiarity with OCR, layout-aware extraction, and table parsing from financial PDFs and Word documents
Familiarity with dbt, Airflow/Dagster, Airbyte/dlt, Snowflake/Databricks
Familiarity with vector databases, RAG pipelines, feature stores, and embedding workflows
Financial services, investment management, or due diligence background is advantageous but not required
Track record of producing technical documentation and training materials

Benefits

Comp & perks

Remote flexibility: work from anywhere in India
Startup energy and flat hierarchy
Opportunity to design for a platform used by financial institutions in 150+ countries
Work with a global team
Direct impact and quick shipping of work