Weekday AI

AWSDataEngineerLead

Gurugram, Haryana, India FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Lead candidates.

The Brief

“AWS Data Engineer – Lead at Weekday AI. Skills: AWS, PySpark, SQL, Data Engineering. Design, develop, and maintain ETL/ELT pipelines. Build and optimize data processing frameworks”

What You'll Achieve.

deliver high-quality data solutions; enable efficient data processing; support analytics and business intelligence initiatives; create reliable and scalable data ecosystems; ensure adherence to best practices; improved reliability and performance

Industry & Context.

Problems you'll solve

analytical and problem-solving abilities

What They're Looking For.

Must Have

AWS Data Engineer experience, leadership responsibilities, PySpark for large-scale data processing, Advanced SQL skills, query optimization, performance tuning, building scalable ETL pipelines, data integration solutions, data warehousing concepts, distributed data processing, structured and unstructured datasets, cloud-native architectures, best practices, Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or a related field

Nice to Have

workflow orchestration tools such as Apache Airflow, CI/CD pipelines, DevOps practices in cloud environments, data governance, security, compliance standards, real-time or streaming data processing frameworks, AWS Certified Data Analytics, AWS Certified Solutions Architect

What You'll Do.

and maintain ETL/ELT pipelines

Build and optimize data processing frameworks

Develop data ingestion and transformation workflows

Manage and optimize data storage

Create efficient SQL queries

Lead implementation of cloud data solutions

and enhance data pipelines

Participate in architecture discussions

Mentor junior engineers

Work in Agile environments

How You'll Work.

Team & Collaboration

collaborate with cross-functional stakeholders; work closely with data architects, analysts, data scientists, and application teams; collaborate with business stakeholders and technical teams; contribute to sprint planning, estimations, and delivery timelines

Communication Scope

excellent communication skills; lead technical discussions

Process & Methodology

Agile development environments, sprint planning, estimations, delivery timelines

Full Job Description

**This role is for one of the Weekday's clients ** Min Experience: 8+ years Location: Gurgaon JobType: full-time We are looking for an experienced and highly skilled AWS Data Engineer – Lead to join our growing data engineering team. The ideal candidate will have deep expertise in designing, building, and optimizing scalable cloud-based data platforms on AWS. This role requires strong hands-on experience with AWS data services, PySpark, and SQL, along with the ability to lead technical initiatives, mentor team members, and collaborate with cross-functional stakeholders to deliver high-quality data solutions. As an AWS Data Engineer Lead, you will play a critical role in architecting modern data pipelines, enabling efficient data processing, and supporting analytics and business intelligence initiatives across the organization. You will work closely with data architects, analysts, data scientists, and application teams to create reliable and scalable data ecosystems. **Requirements** ### Key Responsibilities * Design, develop, and maintain scalable ETL/ELT pipelines using AWS cloud technologies. * Build and optimize large-scale data processing frameworks using PySpark and distributed computing methodologies. * Develop robust data ingestion and transformation workflows using AWS Glue, Lambda, and other AWS-native services. * Manage and optimize data storage and warehousing solutions using Amazon Redshift and Amazon S3. * Create efficient SQL queries, stored procedures, and performance tuning strategies for large datasets. * Lead end-to-end implementation of cloud-based data engineering solutions and ensure adherence to best practices. * Collaborate with business stakeholders and technical teams to understand data requirements and translate them into scalable solutions. * Ensure data quality, integrity, governance, and security across the data platform. * Monitor, troubleshoot, and enhance existing data pipelines for improved reliability and performance. * Participate

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