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Provides technical guidance across the software development lifecycle, including requirements analysis, design, development, testing, documentation, release processes, and feature delivery. Owns complex application features, resolves technical issues, improves code stability and reusability, integrates technologies, estimates work, identifies security and usability issues, delivers training, and mentors software engineers.
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.
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.
Job Description:
Min. 6 and max upto 10.
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
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
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
Good to Have
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
Key Responsibilities
Key Responsibilities
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
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
Location:
DGS India - Pune - Indiqube OrchidBrand:
MerkleTime Type:
Full timeContract Type:
Permanentdentsu Mumbai, Maharashtra, IND Office
Mumbai, India
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