We are looking for an experienced Data Engineer / Analytics Engineer with strong expertise in Databricks Lakehouse, Spark/PySpark, SQL, Delta Lake, and dimensional data modeling. The ideal candidate will have hands-on experience building enterprise-scale data platforms, ETL/ELT pipelines, curated data products, and semantic layers that support business intelligence, analytics, AI, and machine learning use cases.

The role requires a strong combination of data engineering, analytics engineering, platform optimization, and data modeling, along with the ability to work closely with business and technology stakeholders.

Role & responsibilities

- Design and implement up-to-date data architectures using Databricks Lakehouse and Medallion Architecture (Bronze, Silver, Gold).
- Build and maintain scalable ETL/ELT pipelines using Spark, PySpark, SQL, Delta Lake, and Databricks Workflows.
- Develop curated, reusable data products supporting reporting, analytics, AI, and ML use cases.
- Apply Delta Lake optimization techniques including partitioning, Z-Ordering, data skipping, file compaction, and Change Data Feed (CDF).
- Implement CI/CD, Git, DataOps, and DevOps practices for data engineering solutions.

Data Modeling & Analytics Engineering

- Design and maintain scalable dimensional data models, including Star and Snowflake schemas.
- Translate complex business requirements into reliable analytical datasets and reporting solutions.
- Define and standardize business metrics, KPIs, dimensions, hierarchies, and calculations.
- Create reusable datasets, semantic models, and common data assets to improve consistency and reduce duplication.

Semantic & Context Layer

- Design and manage enterprise semantic and context layers to establish a consistent source of truth for analytics and reporting.




- Enable self-service analytics through platforms such as Power BI and Tableau, as well as AI-driven applications.
- Support metric certification, metadata management, lineage, and governance of semantic models.

Performance & Cost Optimization

- Analyze query execution plans and troubleshoot performance bottlenecks.
- Optimize Spark workloads using cluster sizing, autoscaling, caching, broadcast joins, and Adaptive Query Execution (AQE).
- Optimize Delta Lake tables and data models for better query and reporting performance.
- Monitor platform utilization and drive performance and cloud cost efficiencies.

Data Quality & Governance

- Build data quality frameworks, validation controls, automated monitoring, and alerting.
- Support data governance, data cataloging, metadata management, lineage, and data stewardship.
- Ensure data solutions align with enterprise security, governance, regulatory, and quality standards.

Technical Leadership & Collaboration

- Partner with data engineers, analytics engineers, data scientists, analysts, product owners, and business stakeholders.
- Participate in architecture and technical design discussions and recommend engineering best practices.
- Mentor junior engineers and help drive adoption of enterprise data engineering standards and frameworks.
- Contribute to data platform strategy, architecture, and roadmap discussions.
- Comply with applicable company policies, procedures, business directives, and working arrangements.

Role: Other, Industry Type: Pharmaceutical & Life Sciences, Department: Other, Employment Type: Full Time, Permanent Role Category: Other Education UG: B.Sc in Any Specialization, B.C.A. in Any Specialization, B.Tech / B.E. in Any Specialization read more

Key Skills Skills highlighted with ‘‘ are preferred keyskills DatafactoryDelta LakeSparkDatabricks LakehouseSQL and Medallion Architecture (Bronze/Silver/Gold)PySparkAimlData LakeETLData Bricks

📌 Data Engineering Lead (Noida)
🏢 Optum
📍 Noida