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Data Engineer-Data Platforms-AWS

IBM2,108 open roles

Where
Multiple Cities
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Your applicationOpen nowData Engineer-Data Platforms-AWSIBM · Multiple Cities

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The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market. IBM postings stay open a median of 12 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.5%14 days
  5. 35.5%30 days
  6. 45.1%60 days
This job: posted today

IBM median: 12 days open

The posting

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You'll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you'll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You'll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

Job Summary

We are seeking an experienced AWS Data Engineer to design, build, and support scalable, secure, and high-performance data platforms on AWS. The role requires strong hands-on experience with AWS services, distributed data processing frameworks, and SQL-driven analytics.

The ideal candidate will work closely with architects, application teams, and business stakeholders to deliver reliable enterprise-grade data solutions.

Key Responsibilities

• Design, develop, and maintain data pipelines and data platforms on AWS

• Build and operate serverless and containerized workloads using AWS Lambda, EC2, and EKS

• Develop batch and large-scale data processing solutions using Apache Spark

• Implement data ingestion, transformation, and analytics using AWS Glue and Athena

• Write complex and high-performance SQL queries for analytics, reporting, and data validation

• Manage and optimize data storage solutions using AWS S3 and RDS

• Design and manage AWS infrastructure using Infrastructure as Code (IaC) with CloudFormation

• Apply AWS security best practices across IAM, VPC networking, data encryption, and access controls

• Develop clean, scalable, and maintainable code using Python and/or Scala

• Monitor, troubleshoot, and optimize performance, cost, and reliability of data workloads

• Collaborate with cross-functional teams to support analytics, reporting, and regulatory use cases

Mandatory Skills & Qualifications

• Strong hands-on experience with AWS services, including o S3, Lambda, EC2, EKS o AWS Glue, Athena o RDS o VPC, IAM o CloudFormation

• Strong SQL skills, including o Writing complex queries, joins, aggregations, and user defined functions o Performance tuning and query optimization

• Good knowledge of AWS Security, including IAM policies, VPC security, encryption, and least-privilege access

• Hands-on programming experience with Python and/or Scala

• Strong experience with Apache Spark for large-scale data processing

• Good communication skills to explain technical concepts to non-technical stakeholders

• Solid understanding of data engineering concepts such as ETL/ELT, data modeling, and distributed systems

Good to Have / Preferred Skills

• Experience with DBT for data transformations

• Hands-on exposure to Snowflake

• Experience working with Databricks

• Familiarity with modern data lake / Lakehouse architectures

• Experience working in enterprise or regulated environments

Nice-to-Have Competencies

• Strong analytical and problem-solving skills

• Ability to work independently and in distributed teams

• Exposure to AWS Toolset: Experience working with AWS services such as AWS EMR, AWS Glue, Glue Catalog, and Kinesis to design, build, and operate batch and real-time data pipelines.

• Data Pipeline Development: Exposure to developing batch and real-time data pipelines for Data Warehouse and Datalake, utilizing AWS Kinesis and Managed Streaming for Apache Kafka.

• Data Layer Creation: Experience working with AWS RedShift, Aurora, and DynamoDB to create data layers and migrate data using AWS DMS.

• Open Source Technologies: Exposure to utilizing open source technologies like Apache Airflow and dbt, Spark / Python or Spark / Scala on AWS Platform to support data engineering solutions.

• Data Service Management: Experience scheduling and managing data services on the AWS Platform, ensuring efficient operation of data engineering solutions.

• Proficiency with AWS Databrew: Experience working with AWS Glue Databrew to support data engineering solutions, including data preparation and data quality tasks.

• Knowledge of Lambda Functions: Exposure to using Lambda functions with Python to support data engineering solutions, including data processing and data transformation tasks.

• Familiarity with RedShift Spectrum: Experience working with RedShift Spectrum to support data engineering solutions, including data warehousing and data analytics tasks.

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