About Vericence

Vericence is a digital engineering and technology consulting firm helping enterprises build AI-driven platforms, modernize legacy systems, and scale innovation through cloud, data, and intelligent engineering. We partner with global organizations to deliver high-impact technology solutions and build world-class engineering teams.

Job Description

We are seeking a hands-on Data Engineer to help design, build, and operate governed, production-grade data pipelines and AI-ready data products for the Enterprise Data Lakehouse on OCI and Oracle AIDP. In this role, you will work across ingestion, transformation, data quality, metadata, lineage, observability, and production support to deliver trusted Bronze, Silver, and Gold data assets that support analytics, reporting, operational intelligence, GenAI, RAG, and agentic business workflows.

This role reports to the Principal Data Engineering Lead and operates within the Data Governance, AI & Analytics organization as part of the Enterprise Data Lakehouse Engineering team. The ideal candidate is a strong engineer with practical experience building scalable ETL/ELT solutions, applying data governance controls, collaborating with cross-functional teams, and delivering reliable data products in a modern cloud data platform environment.

What You Will Do

Data Pipeline Engineering

- Design, build, and maintain scalable ingestion pipelines that bring structured, semi-structured, and unstructured data from enterprise source systems into the Enterprise Data Lakehouse.
- Develop Bronze, Silver, and Gold transformation pipelines that standardize, cleanse, conform, curate, and publish trusted data assets for enterprise consumption.
- Build reusable ingestion frameworks, transformation patterns, orchestration templates, and onboarding accelerators that improve delivery speed and engineering consistency.
- Optimize pipelines for performance, reliability, maintainability, cost efficiency, observability, and production supportability.
- Collaborate with architects, platform teams, Data Product Owners, governance leads, security teams, and business stakeholders to translate requirements into high-quality data engineering solutions.

Data Quality and Governance Implementation

- Implement data quality validations, schema enforcement, reconciliation controls, automated testing, and exception handling within data pipelines.
- Translate Data Contracts into executable engineering controls, including validation rules, metadata capture, freshness checks, lineage requirements, and quality evidence.
- Capture and maintain technical metadata, source-to-target mappings, lineage, business definitions, operational context, and catalog registration artifacts.
- Apply approved security, masking, privacy,



PHI protection, and access control requirements throughout data processing workflows.
- Ensure data products align with certified business definitions, semantic standards, governance expectations, and enterprise data product publication requirements.

Data Product Development

- Contribute to the design, development, certification, publication, and ongoing support of governed enterprise data products.
- Support data product marketplace readiness by ensuring appropriate metadata, lineage, quality, SLA, security, and documentation artifacts are complete.
- Partner with Data Product Owners and business domain teams to understand consumption needs and deliver reliable, reusable, and AI-ready data assets.
- Continuously improve engineering standards, reusable assets, delivery patterns, and automation across the lakehouse engineering lifecycle.

DataOps and Production Readiness

- Participate in CI/CD workflows, automated deployment, testing, code review, release management, and production-readiness activities.
- Implement logging, monitoring, alerting, data freshness tracking, pipeline health checks, and operational metrics to support production stability.
- Troubleshoot and resolve pipeline failures, performance bottlenecks, data defects, and operational incidents.
- Create and maintain technical documentation, operational runbooks, support procedures, and handoff materials for production support teams.

AI-Assisted Engineering

- Use approved AI-assisted engineering tools to accelerate pipeline development, testing, documentation, migration, and operational artifact creation.
- Apply AI-native engineering practices responsibly, ensuring AI-generated code and deliverables are reviewed, tested, governed, secure, and production-ready.
- Support AI-ready data architecture patterns that enable GenAI, RAG, semantic models, vector search, embeddings, and agentic workflow use cases.

What You Will Deliver

- Production-grade ingestion pipelines that integrate enterprise source systems into the OCI AIDP Lakehouse using repeatable, scalable, and supportable patterns.
- Bronze, Silver, and Gold data assets that are standardized, validated, curated, consumption-ready, and aligned to enterprise architecture and semantic standards.
- Reusable engineering frameworks, templates, orchestration patterns, onboarding accelerators, and code libraries that improve speed,



consistency, and maintainability.
- Executable Data Contract implementations, including schema validation, data quality checks, reconciliation controls, freshness expectations, exception handling, and quality evidence.
- Complete metadata, lineage, catalog registration, source-to-target mapping, security, privacy, and data classification artifacts required for governed data product publication.
- CI/CD-enabled deployment assets, workplace configuration, release documentation, rollback guidance, monitoring implementation, and production support runbooks.
- AI-ready data engineering assets that support analytics, business intelligence, GenAI, RAG, vector search, semantic models, and agentic workflow enablement.

Required Qualifications, Capabilities, and Skills

- 7+ years of experience in Data Engineering, Data Platform Engineering, or a related technical role.
- Strong hands-on development experience with SQL, Python, Spark, PySpark, or distributed data processing frameworks.
- Experience designing, building, testing, deploying, and supporting enterprise ETL/ELT pipelines in cloud or modern data platform environments.
- Understanding of data lakehouse architecture, data modeling, data quality, metadata, lineage, and data governance concepts.
- Experience with Git, CI/CD, automated testing, code reviews, release management, monitoring, and production support practices.
- Strong analytical, troubleshooting, communication, and problem-solving skills with the ability to work effectively in Agile, cross-functional delivery teams.

Preferred Qualifications, Capabilities, and Skills

- Experience with Oracle Cloud Infrastructure and OCI-native data services such as OCI Data Integration, OCI Data Flow, Object Storage, GoldenGate, Autonomous Data Warehouse, or Autonomous AI Lakehouse.
- Experience with modern lakehouse and open data technologies, including Iceberg, Delta Lake, Parquet, ORC, or similar storage and table formats.
- Experience implementing Data Contracts, data product engineering, Data Mesh concepts, governed data marketplaces, metadata management, lineage, cataloging, and data product certification practices.
- Healthcare, payer, claims, provider, member, product, finance, or acquisition integration experience, especially in environments requiring PHI protection, privacy, security, and governed data handling.
- Exposure to AI-ready data architecture patterns supporting GenAI, RAG, semantic models, vector search, embeddings, feature engineering, and agentic workflows.
- Experience with AI-assisted engineering, DataOps, DevOps, observability, infrastructure-as-code concepts, operational documentation, and production support operating models.

📌 Data Engineer (India)
🏢 Vericence
📍 India