Lead Data Engineer (AWS)

Job Description Looking for a hands‑on AWS Technical Lead – Data Engineering with 6 to 10 years of total experience to design and deliver scalable, secure, and high‑performance data platforms on AWS. This role focuses on strong individual contribution with technical ownership, working closely with global teams, architects, and clients to deliver enterprise‑grade data engineering solutions. The position requires deep expertise in AWS data services, SQL, and Python, and the ability to build and optimize reliable data pipelines for analytics and business use cases. Key responsibiltes

Key responsibiltes Data Platform Design & Development Design and implement AWS‑based data engineering solutions aligned to enterprise standards Build and optimize batch and streaming data pipelines using AWS native and open‑source tools Develop SQL‑driven transformations and Python‑based data pipelines for analytics use cases Design efficient data models for performance, scalability, and cost effectiveness Delivery & Quality Ownership Own data engineering deliverables from development through production support Perform performance tuning, cost optimization, and capacity planning Troubleshoot complex data pipeline and production issues, including root‑cause analysis Ensure solutions meet requirements for security, reliability, and scalability

Cloud & Data Engineering (AWS) Strong hands‑on experience with AWS data services, including: Amazon S3, AWS Glue, Athena, Redshift Experience designing cloud‑native data lakes and data warehouse architectures on AWS Deep understanding of batch and streaming data pipelines Experience building scalable, fault‑tolerant data ingestion and transformation workflows SQL & Python (Mandatory) Strong SQL expertise Writing complex SQL for transformations, aggregations, performance tuning, and analytics Hands‑on experience handling large‑scale datasets in Redshift / Athena Strong Python programming skills (mandatory) for data engineering use cases

SQL & Python (Mandatory) Strong SQL expertise Writing complex SQL for transformations, aggregations, performance tuning, and analytics Hands‑on experience handling large‑scale datasets in Redshift / Athena Strong Python programming skills (mandatory) for data engineering use cases PySpark / Spark‑based processing Building reusable ETL components, utilities, and data pipelines Strong understanding of data modeling, transformations, and performance optimization

Delivery & Quality Ownership Own data engineering deliverables from development through production support Perform performance tuning, cost optimization, and capacity planning Troubleshoot complex data pipeline and production issues, including root‑cause analysis Ensure solutions meet requirements for security, reliability, and scalability Collaboration & Client Engagement Work closely with architects, product owners, and client stakeholders Translate business and analytics requirements into robust AWS data engineering solutions Provide clear technical inputs, estimates, and implementation trade‑offs Contribute to solution discussions and technical design reviews Engineering Best Practices Follow and contribute to coding standards, documentation, and data engineering best practices Participate in code reviews and continuous improvement initiatives Ensure adherence to AWS, security, and compliance guidelines

Collaboration & Client Engagement Work closely with architects, product owners, and client stakeholders Translate business and analytics requirements into robust AWS data engineering solutions Provide clear technical inputs, estimates, and implementation trade‑offs Contribute to solution discussions and technical design reviews Engineering Best Practices Follow and contribute to coding standards, documentation, and data engineering best practices Participate in code reviews and continuous improvement initiatives Ensure adherence to AWS, security, and compliance guidelines Must have skills

Must have skills Cloud & Data Engineering (AWS) Strong hands‑on experience with AWS data services, including: Amazon S3, AWS Glue, Athena, Redshift Experience designing cloud‑native data lakes and data warehouse architectures on AWS Deep understanding of batch and streaming data pipelines Experience building scalable, fault‑tolerant data ingestion and transformation workflows SQL & Python (Mandatory) Strong SQL expertise Writing complex SQL for transformations, aggregations, performance tuning, and analytics Hands‑on experience handling large‑scale datasets in Redshift / Athena

Good to have skills Experience with real‑time/streaming technologies (Kinesis, Kafka, MSK) Exposure to Lakehouse architectures and modern data platform patterns Experience integrating AWS data platforms with BI and analytics tools Knowledge of data governance, data quality, and metadata management Familiarity with FinOps practices for optimizing AWS data platform costs Exposure to marketing, customer, or analytics data domains (CDP / MarTech) Experience working in Agile delivery models with global delivery exposure Education Qulification

Certification If Any AWS Data Analytics / Solutions Architect Any two of the above Databricks, Snowflake, or other cloud data platform certifications are a plus. Shift timing

Data Processing & Engineering Proven hands‑on experience with distributed processing frameworks such as Spark / PySpark Experience working with structured, semi‑structured, and unstructured data Solid understanding of schema design, partitioning, and query optimization DevOps & Platform Engineering Experience with Infrastructure as Code using Terraform and/or CloudFormation Hands‑on experience building and maintaining CI/CD pipelines for data platforms Exposure to containerized workloads (Docker, ECS/EKS where applicable to data workloads) Collaboration & Ownership Strong ownership mindset for solution quality, performance, and production stability Excellent communication skills to collaborate with architects, DevOps, QA, and business stakeholders

Strong Python programming skills (mandatory) for data engineering use cases PySpark / Spark‑based processing Building reusable ETL components, utilities, and data pipelines Strong understanding of data modeling, transformations, and performance optimization Data Processing & Engineering Proven hands‑on experience with distributed processing frameworks such as Spark / PySpark Experience working with structured, semi‑structured, and unstructured data Solid understanding of schema design, partitioning, and query optimization

PySpark / Spark‑based processing Building reusable ETL components, utilities, and data pipelines Strong understanding of data modeling, transformations, and performance optimization Data Processing & Engineering Proven hands‑on experience with distributed processing frameworks such as Spark / PySpark Experience working with structured, semi‑structured, and unstructured data Solid understanding of schema design, partitioning, and query optimization DevOps & Platform Engineering Experience with Infrastructure as Code using Terraform and/or CloudFormation Hands‑on experience building and maintaining CI/CD pipelines for data platforms Exposure to containerized workloads (Docker, ECS/EKS where applicable to data workloads)