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The Cigna Group
Data Analytics Operations Lead Engineer -HIH- Evernorth
The Cigna Group
Hyderabad•On-site•5-8 Yrs•3 days ago
Job description
Overview
Lead Data Engineer – Platinum Data Foundations, Lineage & Observability
Responsibilities
- Partner with Data Operations Analysts to perform data foundation assessments for Platinum data sources, evaluating:
- Source system readiness and integration patterns
- Data model structure and reuse potential
- Reliability, scalability, and quality controls
- Design, build, and maintain enterprise‑grade data pipelines that support certified data products.
- Ensure Platinum sources meet standards for performance, availability, and operational resilience.
- Implement and maintain end‑to‑end technical data lineage from source systems through Databricks and downstream consumption layers.
- Build and maintain integrations between data products and enterprise data governance tools (e.g., Collibra) to:
- Publish technical metadata and lineage
- Associate data assets with governed business metadata
- Enable automated certification and impact analysis workflows
- Partner with DataOps and governance teams to ensure engineered metadata aligns with enterprise standards and stewardship processes.
- Design and implement automated data observability and reliability reporting for Platinum data sources, including:
- Freshness, volume, and anomaly tracking
- Pipeline health and dependency monitoring
- SLA and reliability indicators
- Integrate observability outputs into governance and metadata platforms where appropriate to support transparency and auditability.
- Proactively identify and remediate data quality and operational issues.
- Build and optimize pipelines and transformations within Databricks, following best practices for performance, cost, and scalability.
- Connect and integrate data across Databricks and other enterprise data platforms, including warehouses, lakehouses, and operational data stores.
- Support the creation of curated, reusable data layers aligned to Platinum data product definitions and certified metrics.
- Work closely with Data Operations Analysts to translate data specifications, lineage requirements, and business metadata into engineered solutions.
- Partner with analytics teams to ensure Platinum data assets are fit for reporting, metrics, and advanced analytics.
- Collaborate with platform, architecture, and governance teams to ensure enterprise standards are consistently implemented.
Qualifications
- 5–8 years in Data Engineering, Analytics Engineering, or Enterprise Data Platforms
- Role Summary
- The Senior Data Engineer is responsible for building, assessing, and strengthening the data foundations that support Platinum‑level enterprise data sources. This role works in close partnership with Data Operations Analysts to operationalize data specifications, implement lineage and observability capabilities, and ensure critical enterprise data assets are reliable, transparent, and governed.
- A key component of this role is engineering integrations between Platinum data products and enterprise data governance platforms (e.g., Collibra) to automate lineage, technical metadata, and observability signals across the data ecosystem.
- 5–8 years of experience in data engineering or analytics engineering roles.
- Strong hands‑on experience with Databricks and modern lakehouse architectures.
- Proven experience building enterprise‑grade data pipelines and integrations.
- Demonstrated experience implementing:
- Technical lineage and metadata publishing
- Integrations between data platforms and data governance tools (preferably Collibra)
- Data observability and reliability controls
- Strong understanding of enterprise data integration and governance patterns.
- Ability to work closely with DataOps, governance, and analytics teams in a matrixed environment.
- Familiarity with Collibra APIs, metadata ingestion frameworks, or lineage automation patterns.
- Data engineering and pipeline architecture
- Lineage and metadata integration engineering
- Observability and reliability enablement
- Governance‑aware solution design
- Cross‑functional collaboration and execution