Tidyhire
Lead Data Engineer
- Compensation
- Not disclosed
- Location
- Hyderabad
- Work setup
- Not specified
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Data engineering
- Posted
Skills
Full description
Job Description : Key Responsibilities :
- Lead enterprise data engineering initiatives across cloud, SAP, and SAS ecosystems
- Design scalable and secure enterprise data platforms and integration frameworks
- Develop and optimize ETL/ELT pipelines for SAP, SAS, and enterprise applications
- Enable SAP data ingestion into cloud analytics and AI platforms
- Drive SAS modernization and migration strategies toward modern data platforms
- Build reusable data frameworks, ingestion standards, and engineering best practices
- Collaborate with business, SAP functional, analytics, and architecture teams
- Ensure high availability, reliability, observability, and optimization of data pipelines
- Establish governance, lineage, quality, and security controls across data platforms
- Mentor engineering teams and lead technical solution discussions
- Support enterprise analytics, reporting, AI/ML, and self-service BI initiatives
- Contribute to enterprise digital transformation and data modernization roadmap
Required Skills :
- 8+ years of experience in Data Engineering and Analytics Engineering
- Strong expertise in Python, SQL, Spark/PySpark
- Hands-on experience with cloud platforms Microsoft Azure
- Experience with modern data platforms like Databricks, Snowflake, and data lake/lakehouse architecture
- Strong experience in SAP Data Engineering and SAP data extraction frameworks
- Hands-on experience with SAP ECC, S/4HANA, BW/BW4HANA, SAP SLT, SAP CDS Views, or SAP Datasphere
- Experience integrating SAP data into enterprise cloud data platforms
- Strong SAS programming and SAS data engineering expertise
- Experience in SAS ETL, SAS DI Studio, SAS Base, SAS Macros, and migration/modernization from SAS to cloud platforms
- Expertise in ETL/ELT pipeline development and orchestration
- Experience with batch and streaming data pipelines
- Hands-on experience with orchestration tools such as Apache Airflow or Azure Data Factory
- Strong knowledge of data warehousing, dimensional modeling, and metadata management
- Experience with DevOps, CI/CD, and Git-based deployment practices
- Understanding of data governance, security, lineage, and quality controls