Lead AWS Data Engineer || Hybrid || TOP MNC ||

Senior Data Engineer

This role requires deep technical expertise in AWS data services, modern data architecture, and a passion for delivering reliable, high-quality data solutions at scale.

Total experience:

Key Responsibilities

• Architect and implement scalable, fault-tolerant data pipelines using AWS Glue, Lambda, EMR, Step Functions, and Redshift
• Build and optimize data lakes and data warehouses on Amazon S3, Redshift, and Athena
• Develop Python-based ETL/ELT frameworks and reusable data transformation modules
• Integrate multiple data sources (RDBMS, APIs, Kafka/Kinesis, SaaS systems) into unified data models
• Lead efforts in data modeling, schema design, and partitioning strategies for performance and cost optimization
• Drive data quality, observability, and lineage using AWS Data Catalog, Glue Data Quality, or third-party tools
• Define and enforce data governance, security, and compliance best practices (IAM policies, encryption, access control)
• Collaborate with cross-functional teams (Data Science, Analytics, Product, DevOps) to support analytical and ML workloads
• Implement CI/CD pipelines for data workflows using AWS CodePipeline, GitHub Actions, or Cloud Build
• Provide technical leadership, code reviews, and mentoring to junior engineers
• Monitor data infrastructure performance, troubleshoot issues, and lead capacity planning
  

Required Skills & Qualifications

• Bachelors or Master’s degree in Computer Science, Information Systems, or related field
• 5–10 years of hands-on experience in data engineering or data platform development
• Expert-level proficiency in Python (pandas, PySpark, boto3, SQLAlchemy)
• Advanced experience with AWS Data Services, including:

AWS Glue, Lambda, EMR, Step Functions, DynamoDB, EDW Redshift, Athena, S3, Kinesis, Amazon Quicksight.

IAM, CloudWatch, CloudFormation / Terraform (for infrastructure automation)

• Strong experience in SQL, data modeling, and performance tuning
• Proven ability to design and deploy data lakes, data warehouses, and streaming solutions
• Solid understanding of ETL best practices, partitioning, error handling, and data validation
• Hands-on experience in version control (Git) and CI/CD for data pipelines
• Knowledge of containerization (Docker/Kubernetes) and DevOps concepts
• Excellent analytical, debugging, and communication skills

Preferred / Nice-to-Have Skills

• Experience with Apache Spark or PySpark on AWS EMR or Glue
• Familiarity with Airflow, dbt, or Dagster for workflow orchestration
• Exposure to real-time data streaming (Kafka, Kinesis Data Streams, or Firehose)
• Knowledge of Lake Formation, Glue Studio, or DataBrew
• Experience integrating with machine learning and analytics platforms (SageMaker, QuickSight)
• Certification: AWS Certified Data Analytics – Specialty or AWS Certified Solutions Architect

Soft Skills

• Strong ownership mindset with focus on reliability and automation
• Ability to mentor and guide data engineering teams
• Effective communication with both technical and non-technical stakeholders
• Comfortable working in agile, cross-functional teams