Cloud Engineer

Important Note (Please Read Before Applying)

🚫 Do NOT apply if:

• You have less than 5 years of AWS Data Engineer experience.
• You do not have hands-on AWS Data Services experience.
• You are on a notice period longer than 15 days.
• You are looking for remote only (role is

  hybrid in Hyderabad

  ).
• You are a fresher or from unrelated backgrounds (e.g., Java-only, Testing-only, Support).

ALL criteria

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About the Company

Our client is a trusted global innovator of IT and business services, present in 50+ countries. They specialize in digital & IT modernization, consulting, managed services, and industry-specific solutions. With a commitment to long-term success, they empower clients and society to move confidently into the digital future.

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

● Bachelor’s or Master’s degree in Computer Science, Information Systems, or related field

● 5 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

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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

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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