AWS Engineer

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Job Description

We are looking for a highly motivated Data Engineer with 3–4 years of hands-on experience in AWS Databricks to join our data platform team. The role involves working closely with data scientists, analysts, architects, and business stakeholders to design, build, and maintain scalable, high-performance data pipelines and data solutions within the AWS cloud environment.

The ideal candidate should have strong expertise in Databricks, PySpark, SQL, Delta Lake, and AWS data services, along with a good understanding of modern data engineering practices, orchestration, and data governance.

• Design, develop, test, and deploy scalable ETL/ELT pipelines using AWS Databricks, PySpark, and SQL.
• Build and maintain robust data pipelines for ingesting and processing structured and unstructured data from multiple sources such as Amazon S3, Amazon RDS, APIs, and external platforms.
• Implement and manage Delta Lake solutions to support both batch and streaming data processing.
• Optimize Spark workloads for performance, scalability, and cost efficiency.
• Ensure data quality, consistency, reliability, and integrity through validations, monitoring, exception handling, and reconciliation checks.
• Collaborate with cross-functional teams including data analysts, data modelers, architects, and business stakeholders to support reporting, analytics, and downstream consumption.
• Support orchestration and scheduling of pipelines using tools such as AWS Glue Workflows, Apache Airflow, or AWS Step Functions.
• Participate in CI/CD implementation and code deployment processes using Git, AWS CodePipeline, and CodeBuild.
• Apply best practices related to data governance, metadata management, security, and access control.
• Support production issue resolution, ad hoc data requests, and continuous improvement of the data platform.

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related discipline.
• 3–4 years of experience in Data Engineering or a similar role.
• Strong hands-on experience with AWS Databricks.
• Good proficiency in PySpark and SQL for large-scale data processing.
• Experience working with Delta Lake and modern lakehouse concepts.
• Familiarity with AWS services such as Amazon S3, Amazon RDS, AWS Glue, AWS Lambda, and Amazon Redshift.
• Knowledge of batch and streaming data architectures.
• Experience with workflow orchestration, monitoring, and debugging of pipelines.
• Understanding of version control and CI/CD practices.
• Awareness of data security, governance, and metadata management principles.
• Strong analytical, problem-solving, and communication skills.

• Experience in building data pipelines in enterprise-scale cloud data platforms.
• Familiarity with real-time / streaming frameworks and event-driven architectures.
• Exposure to data quality frameworks, reconciliation processes, and operational monitoring.
• Working knowledge of infrastructure automation and cloud deployment best practices.
• Experience collaborating in agile delivery environments.

• Strong problem-solving orientation
• Attention to detail and focus on data quality
• Ability to work in a fast-paced, collaborative environment
• Good stakeholder communication and coordination skills
• Ownership mindset with a focus on delivery excellence

• Experience with data modeling and analytics enablement
• Exposure to DevOps and platform engineering practices
• Certification in AWS or Databricks technologies

This role offers the opportunity to work at the forefront of cloud-based data engineering, partnering with talented data scientists, analysts, and architects to build scalable, high-impact data solutions on AWS Databricks.

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

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