Company
AI & Data Engineering : Data Engineering
LeadDataEngineer
Neural analysis suggests this role is
optimal for Lead candidates.
“Lead Data Engineer. Skills: Data Engineering, Cloud Data Platform, Data Warehousing, AWS. Design data pipelines. Develop data pipelines”
Industry & Context.
Analytical capabilities; Problem-solving capabilities
What They're Looking For.
Must Have
8–10 years of experience in Data Engineering, 8–10 years of experience in Data Warehousing, 8–10 years of experience in Cloud Data Platform development, Proven experience delivering enterprise-scale data engineering solutions in cloud environments, AWS Cloud Technologies hands-on experience, Advanced proficiency in Python, Advanced proficiency in PySpark, Advanced proficiency in SQL, Advanced proficiency in PL/SQL, Experience building and optimizing ETL/ELT pipelines, Experience building and optimizing data ingestion frameworks, Solid understanding of data warehousing concepts, Solid understanding of dimensional modeling, Solid understanding of database design, Experience working with structured data, Experience working with semi-structured data, Experience working with unstructured data, Experience designing scalable data solutions, Experience designing secure data solutions, Experience designing high-performance data solutions, Knowledge of data quality frameworks, Knowledge of data governance practices, Knowledge of data lifecycle management, Experience with workflow orchestration, Experience with event-driven architectures, Experience with distributed data processing
Nice to Have
Experience supporting Data Science initiatives, Experience supporting Machine Learning initiatives, Experience supporting AI initiatives, Experience supporting Advanced Analytics initiatives, Exposure to modern Data Lakehouse architectures, Experience with CI/CD pipelines, Experience with DevOps practices, Experience with Infrastructure as Code (IaC), Experience working in Agile/Scrum delivery environments, AWS Certified Data Engineer, AWS Solutions Architect certification, Equivalent cloud certifications
What You'll Do.
Design data pipelines
Develop data pipelines
Maintain data pipelines
Design ETL/ELT frameworks
Develop ETL/ELT frameworks
Maintain ETL/ELT frameworks
Design data integration solutions
Develop data integration solutions
Maintain data integration solutions
Build cloud-native data solutions
Develop data processing workflows
Optimize data processing workflows
Design data warehouse solutions
Implement data warehouse solutions
Implement data models
Understand business requirements
Deliver high-quality data solutions
Ensure data integrity
Ensure data governance
Ensure data compliance
Optimize database performance
Optimize query execution
Optimize large-scale data processing workloads
Implement monitoring mechanisms
Implement alerting mechanisms
Implement troubleshooting mechanisms
Participate in solution design discussions
Participate in architecture reviews
Participate in cloud modernization initiatives
Mentor junior team members
Promote engineering best practices
Promote code quality standards
Promote knowledge sharing
Support production deployments
Support issue resolution
Support continuous improvement activities
How You'll Work.
Team & Collaboration
Cross-functional teams; Business stakeholders; Product teams; Data scientists; Analysts
Communication Scope
Stakeholder management
Process & Methodology
Agile, Scrum
Full Job Description
## Description Lead Data Engineer ## Primary Skills ETL Fundamentals, SQL, BigQuery, Dataproc, SQL (Basic + Advanced), Python, Data Catalog, Data Warehousing, Composer, Dataflow, Cloud Trace, Cloud Logging, Cloud Storage, Datafusion, Modern Data Platform Fundamentals, Data Modelling Fundamentals, PLSQL, T-SQL, Stored Procedures ## Specialization AWS Data Engineering Basic: Senior Data Engineer ## Job requirements We are seeking a highly skilled and motivated Senior Data Engineer with 8–10 years of experience in designing, building, and maintaining scalable cloud-based data platforms. The ideal candidate will have strong expertise in AWS data services, modern data engineering practices, and data warehousing solutions. This role requires a hands-on engineer who can collaborate with cross-functional teams, translate business requirements into technical solutions, and drive the development of robust, high-performance data pipelines that support analytics, reporting, and AI/ML initiatives. Key Responsibilities Design, develop, and maintain scalable and reliable data pipelines, ETL/ELT frameworks, and data integration solutions. Build cloud-native data solutions using AWS services including Glue, Lambda, Redshift, Aurora, OpenSearch, Step Functions, SNS, and S3. Develop and optimize data processing workflows using Python, PySpark, SQL, and PL/SQL. Design and implement data warehouse solutions, data marts, and data models to support enterprise reporting and analytics. Work closely with business stakeholders, product teams, data scientists, and analysts to understand requirements and deliver high-quality data solutions. Ensure data quality, integrity, governance, security, and compliance across the data ecosystem. Optimize database performance, query execution, and large-scale data processing workloads. Implement monitoring, alerting, and troubleshooting mechanisms to ensure platform reliability and operational excellence. Participate in solution design discussions, archite
Applying for this Lead Data Engineer role?
Most applicants get filtered before a human reads their resume. See if yours makes the cut.
How to Apply on Lever
- Lever uses a streamlined one-page form — apply in under 5 minutes.
- LinkedIn import works well; review parsed data before submitting.
- The cover letter field is optional but visible to reviewers — use it to differentiate.
- Referral codes from employees can significantly boost visibility of your application.
ANONYMOUS · UNFILTERED
What do employees actually say about this company?
Real rants from real employees. Read before you apply.