AWS Data Engineer

Important Note (Please Read Before Applying)

Do

• You have

  less than 6 years

  or

  more than 17 years

  of experience
• You do not have

  hands-on AWS Data Engineer 5+ experience

• You are on a

  notice period longer than 15 days

• You are looking for

  remote only

• You are a

  fresher or unrelated background (e.g., support, testing only, non-Java roles)

ONLY if

ALL criteria

Job Title:

Location: Hyderabad

Experience:

Employment Type:

Notice Period:

About the Company

Our client is a trusted global innovator of IT and business services, present in 10+ 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.

Job description:

Job Overview:

We are looking for an experienced Senior Data Engineer to design, build, and

optimize scalable, high-performance data platforms using AWS cloud services and

Python. The ideal candidate will play a key role in architecting end-to-end data

pipelines, driving automation, ensuring data quality, and enabling analytics and AI

workloads across the organization.

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:6 to 17 years

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

● Bachelor’s 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