We are looking for an experienced Lead Data Engineer to design and build an enterprise HR Data Lake / Data Warehouse using Snowflake and Databricks. The role will serve as a technical SME for HR data, owning data architecture, modelling, security, data-sharing patterns, and AI/ML readiness.

The ideal candidate will have strong hands-on experience with Snowflake, Databricks, HR/HCM data, and enterprise data engineering, with experience working with platforms such as Oracle HCM, Workday, SAP SuccessFactors, or similar HCM systems. The role will involve close collaboration with HR, data ingestion, platform, AI/ML, and business teams.

Key Responsibilities :

- Design and implement scalable HR Data Lake / Data Warehouse architecture using Snowflake and Databricks.

- Act as the technical SME for HR/HCM data and translate business requirements into scalable data solutions.

- Design enterprise data models for employee, workforce, organizational, compensation, talent, recruitment, and other HR datasets.

- Define data architecture, modelling standards, data domains, data flows, and integration patterns.

- Design and implement data ingestion pipelines from Oracle HCM, Workday, SAP SuccessFactors, and other HCM/enterprise systems.

- Develop robust ETL/ELT pipelines for structured and semi-structured HR data.

- Implement data transformation, cleansing, validation, reconciliation, and quality frameworks.

- Design appropriate Snowflake schemas, tables, views, warehouses, and performance optimization strategies.

- Leverage Databricks for data engineering, transformation, processing, and analytics workloads.

- Establish data security, access controls, privacy, and governance mechanisms for sensitive HR data.

- Define data-sharing and consumption patterns across HR, analytics, reporting, and downstream applications.

- Ensure HR data architecture supports AI/ML and advanced analytics use cases.

- Collaborate with ingestion, data platform, AI/ML, security, and business teams to ensure end-to-end delivery.

- Define data quality, lineage, metadata, and governance standards across HR data domains.

- Optimize data pipelines and Snowflake/Databricks workloads for performance, scalability, reliability, and cost.

- Support development of reusable data engineering frameworks and best practices.

- Troubleshoot complex data, pipeline, integration, and platform issues and drive root-cause resolution.

- Provide technical leadership, conduct design and code reviews, and mentor data engineering resources.

- Maintain architecture documentation, technical specifications, data models, and operational documentation.

Required Skills & Experience :

- 8 - 13 years of experience in data engineering, data architecture, or related technology roles.

- Strong hands-on experience with Snowflake and enterprise data warehouse/data lake architectures.

- Strong experience with Databricks and modern data engineering platforms.

- Proven experience working with HR/HCM data in enterprise environments.

- Experience with one or more HCM platforms such as Oracle HCM, Workday, SAP SuccessFactors, or similar.

- Strong understanding of HR data domains, relationships, data structures, and business processes.

- Experience designing HR Data Lakes, Data Warehouses, or enterprise HR data platforms.

- Strong knowledge of data modelling, dimensional modelling, ETL/ELT, data integration, and data transformation.

- Hands-on experience building scalable data pipelines and working with large datasets.

- Strong SQL skills and proficiency in a programming language such as Python.

- Experience with Snowflake performance optimization, workload management, and cost optimization.

- Good understanding of data security, access controls, privacy, governance, lineage, and data quality.

- Experience designing data-sharing and consumption patterns for analytics and downstream applications.

- Understanding of AI/ML data readiness, including data preparation, feature/data products, and governed access to enterprise data.

- Experience working with cross-functional teams across data engineering, platform, AI/ML, HR, and business functions.

- Strong technical leadership, architecture, problem-solving, and stakeholder management skills.