1 to 25 of 26 Amazon EMR Jobs

Hands-on experience with Big Data ecosystems Hadoop, Spark, Kafka, Hive, HBase, etc. Strong experience with Cloud platforms (AWS/Azure/GCP) and services like: AWS: S3, Glue, EMR, Redshift, Lambda, Kinesis Azure: Data Factory, Synapse, Databricks, ADLS GCP: BigQuery, Dataflow, Pub/Sub Experience with Data Warehouse/Data Lake/Lakehouse design and modeling (Kimball, OLAP More ❯

solutions. Strong hands-on experience with at least one major cloud platform (AWS, Azure, or Google Cloud Expertise in cloud-native data services such as AWS Glue, Lambda, S3, EMR, Redshift, Lake Formation, Azure Synapse, Data Factory, Databricks, or BigQuery. Advanced knowledge of SQL, Python, Spark, PySpark, and distributed data frameworks. Proven background in building ELT/ETL systems More ❯

business decisions. Responsibilities for the AWS Data Engineer: Design, build and maintain scalable data pipelines and architectures within the AWS ecosystem Leverage services such as AWS Glue, Lambda, Redshift, EMR and S3 to support data ingestion, transformation and storage Work closely with data analysts, architects and business stakeholders to translate requirements into robust technical solutions Implement and optimise ETL More ❯

the following: Python, SQL, Java Commercial experience in client-facing projects is a plus, especially within multi-disciplinary teams Deep knowledge of database technologies: Distributed systems (e.g., Spark, Hadoop, EMR) RDBMS (e.g., SQL Server, Oracle, PostgreSQL, MySQL) NoSQL (e.g., MongoDB, Cassandra, DynamoDB, Neo4j) Solid understanding of software engineering best practices - code reviews, testing frameworks, CI/CD, and code More ❯

Ability to design, build, and optimise scalable data models that support analytics and machine learning workloads. Solid working knowledge of AWS data services (e.g., S3, Kinesis, Glue, Redshift, Lambda, EMR) or Azure equivalents (e.g., ADF, Synapse, Fabric, Azure Functions). Familiarity with Palantir Foundry or Gotham is a significant advantage. Experience working within Data Lakehouse platforms such as Databricks More ❯

Ability to design, build, and optimise scalable data models that support analytics and machine learning workloads. Solid working knowledge of AWS data services (e.g., S3, Kinesis, Glue, Redshift, Lambda, EMR) or Azure equivalents (e.g., ADF, Synapse, Fabric, Azure Functions). Experience working within Data Lakehouse platforms such as Databricks, Snowflake, and/or Microsoft Fabric is an advantage. Core More ❯

in AWS cloud technologies for ETL pipeline, data warehouse and data lake design/building and data movement. AWS data and analytics services (or open-source equivalent) such as EMR, Glue, RedShift, Kinesis, Lambda, DynamoDB. What you can expect Work to agile best practices and cross-functionally with multiple teams and stakeholders. You’ll be using your technical skills More ❯

in AWS cloud technologies for ETL pipeline, data warehouse and data lake design/building and data movement. AWS data and analytics services (or open-source equivalent) such as EMR, Glue, RedShift, Kinesis, Lambda, DynamoDB. What you can expect Work to agile best practices and cross-functionally with multiple teams and stakeholders. You’ll be using your technical skills More ❯

in AWS cloud technologies for ETL pipeline, data warehouse and data lake design/building and data movement. AWS data and analytics services (or open-source equivalent) such as EMR, Glue, RedShift, Kinesis, Lambda, DynamoDB. What you can expect Work to agile best practices and cross-functionally with multiple teams and stakeholders. You’ll be using your technical skills More ❯

data pipelines. Collaborate with data scientists and analysts to ensure data quality, availability, and consistency for advanced modeling and reporting. Utilize AWS or other cloud services (e.g., S3, Glue, EMR, Snowflake) to architect and maintain cloud-based data ecosystems. Write and optimize complex SQL queries for data extraction, integrity checks, and performance tuning. Required Technical Skills 5+ years of More ❯

years in a technical leadership or management role Strong technical proficiency in data modelling, data warehousing, and distributed systems Hands-on experience with cloud data services (AWS Redshift, Glue, EMR or equivalent) Solid programming skills in Python and SQL Familiarity with DevOps practices (CI/CD, Infrastructure as Code - e.g., Terraform) Excellent communication skills with both technical and non More ❯

Skills • Min of 2 years of experience in data engineering or a similar role. • Hands-on experience with core AWS data services (for example S3, Glue, Athena, Lambda, IAM, EMR). • Strong SQL skills (joins, window functions, optimization). • Solid Python for data processing. • Experience building production ETL/ELT pipelines. • Working knowledge of security and IAM (roles, policies More ❯

and delivering production-grade software and data systems. Proficiency in Python, Java, or Scala - comfortable writing robust, testable, and scalable code. Deep experience with AWS (Lambda, ECS/EKS, EMR, Step Functions, S3, IAM, etc.). Strong knowledge of distributed systems and streaming/data pipelines (Kafka, Spark, Delta, Airflow, etc.). Familiarity with infrastructure-as-code (Terraform, CloudFormation More ❯

SQL, Scala, or Java 4+ years of experience with a public cloud (AWS, Microsoft Azure, Google Cloud) 5+ years experience with Distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL) 4+ year experience working on real-time data and streaming applications 4+ years of experience with NoSQL implementation (Mongo, Cassandra) 4+ years of data More ❯

SQL, Scala, or Java 4+ years of experience with a public cloud (AWS, Microsoft Azure, Google Cloud) 4+ years experience with Distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL) 4+ year experience working on real-time data and streaming applications 4+ years of experience with NoSQL implementation (Mongo, Cassandra) 4+ years of data More ❯

SQL, Scala, or Java 4+ years of experience with a public cloud (AWS, Microsoft Azure, Google Cloud) 4+ years experience with Distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL) 4+ year experience working on real-time data and streaming applications 4+ years of experience with NoSQL implementation (Mongo, Cassandra) 4+ years of data More ❯

SQL, Scala, or Java 4+ years of experience with a public cloud (AWS, Microsoft Azure, Google Cloud) 4+ years experience with Distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL) 4+ year experience working on real-time data and streaming applications 4+ years of experience with NoSQL implementation (Mongo, Cassandra) 4+ years of data More ❯

CloudWatch, and CloudTrail. Ensure designs follow AWS Well-Architected Framework principles (security, cost, performance, reliabilityData Engineering & Pipelines Build and optimize data pipelines in AWS using Glue, Lambda, Step Functions, EMR, Athena, and S3. Implement DAG-based orchestration using Apache Airflow, AWS Managed Workflows (MWAA), or Glue Workflows. Ensure data quality, reliability, lineage, and observability across all pipelines.Machine Learning Pipeline … OLTP, OLAP, lakehouse) and architectural patterns. Experience with CI/CD pipelines and DevOps on AWS.Preferred Qualifications AWS certifications (e.g., AWS Certified Data Analytics Specialty, Solutions Architect Experience with EMR, Redshift, Kinesis, or Kafka. Knowledge of MLOps tools (SageMaker, MLflow, Feature Stores Familiarity with IaC (Terraform, CloudFormation Experience working in enterprise-scale, highly regulated environments.Soft Skills Strong communication and More ❯

optimization. Experience with ServiceNow for incident/change/problem management. Excellent analytical, troubleshooting, and communication skills. Nice to Have Exposure to cloud-based Big Data platforms (e.g., AWS EMR Familiarity with containerization (Docker, Kubernetes) and infrastructure automation tools (Ansible, Terraform Note: If you are interested, please share your updated resume and suggest the best number & time to connect More ❯

projects. Required Skills & Qualifications: Demonstrable experience in building data pipelines using Spark or Pandas. Experience with major cloud providers (AWS, Azure, or Google). Familiarity with big data platforms (EMR, Databricks, or DataProc). Knowledge of data platforms such as Data Lakes, Data Warehouses, or Data Meshes. Drive for self-improvement and eagerness to learn new programming languages. Ability More ❯

programming languages like Java, or Python development. 2+ years hands-on with AWS Lambda, SNS/SQS, S3, KMS, IAM, CloudWatch, EC2, RDS and DynamoDB, AWS CLI, Cloud 9, EMR SPARK, Glue, Athena, Lake Formation Fluency with CI/CD toolsets such as: CloudFormation, BitBucket Working knowledge of AWS Serverless architecture Working knowledge of Relational database management systems and … programming languages like Java, or Python development. 2+ years hands-on with AWS Lambda, SNS/SQS, S3, KMS, IAM, CloudWatch, EC2, RDS and DynamoDB, AWS CLI, Cloud 9, EMR SPARK, Glue, Athena, Lake Formation Fluency with CI/CD toolsets such as: CloudFormation, BitBucket Working knowledge of AWS Serverless architecture Working knowledge of Relational database management systems and … data integration tools. IDEAL CANDIDATE: - AWS- Redshift, EMR, Athena, - 3 plus years - Python/C++ or any programming language background _ SQL/Oracle or SQL Server experience plus on Data analytics, data mgt, Data Warehouse knowledge. More ❯

management with proven success in largescale data platform delivery Deep expertise in MDM systems ETL tools and data integration frameworks Strong handson experience with AWS services EC2 S3 Glue EMR Lambda IAM CloudFormation etc Familiarity with PySpark Kafka TALEND and CICD pipelines Jenkins GitHub Experience in data governance metadata management and data quality frameworks Excellent communication stakeholder engagement and More ❯

What We’re Looking For Experience building data pipelines using Spark or Pandas . Familiarity with major cloud platforms (AWS, Azure, or GCP). Understanding of big data tools (EMR, Databricks, DataProc). Knowledge of data architectures (Data Lakes, Warehouses, Mesh). A proactive mindset with a passion for learning new technologies. Nice-to-Have Skills Automated data quality More ❯

lake infrastructure using Apache Iceberg, creating distributed systems for efficient storage and transformations. Take end-to-end ownership of the complete data lifecycle, from Kafka ingestion to Spark/EMR transformations, enabling AI-powered analysis. The ideal candidate: 7+ years of software engineering experience with at least 4+ years focused specifically on data engineering, demonstrating strong software engineering skills. More ❯

to shape it with us. Your role will involve: Designing and developing scalable, testable data pipelines using Python and Apache Spark Orchestrating data workflows with AWS tools like Glue, EMR Serverless, Lambda, and S3 Applying modern software engineering practices: version control, CI/CD, modular design, and automated testing Contributing to the development of a lakehouse architecture using Apache … building ETL pipelines Has experience with or is eager to learn Apache Spark for large-scale data processing Is familiar with the AWS data stack (eg S3, Glue, Lambda, EMR) Enjoys learning the business context and working closely with stakeholders Works well in Agile teams and values collaboration over solo heroics More ❯