AWS Data Engineer

Location: Newark, NJ | Experience: 3–9 years

About the Company

We are a company focused on helping enterprise clients transform performance through analytics, AI, cloud technologies and digital engineering. Our teams partner with global organizations to build practical data and technology solutions that improve decision-making, modernize platforms and support measurable business outcomes. This role sits within an environment that works on cloud-native data platforms for analytics, reporting, AI/ML and business intelligence use cases. The team values engineering quality, scalable design and disciplined delivery in production settings. We operate with a global mindset and a collaborative approach, bringing together technical specialists who can turn business needs into reliable data solutions. The work involves modern cloud services, distributed processing and strong attention to governance, security and reliability. The opportunity is suited to someone who wants to work on enterprise-scale cloud and analytics transformation initiatives while building deep capability in AWS data engineering.

Company Proof Points

• Partners with global enterprises to deliver technology solutions that improve business outcomes and support analytics-led transformation. - Builds cloud-native data platforms for enterprise analytics, reporting, AI/ML and business intelligence initiatives. - Works in an environment that emphasizes production reliability, scalable engineering and modern cloud technologies.

Company Practices & Ways of Working

• Collaborative global delivery environment with teams working across cloud, data and analytics disciplines. - Strong focus on continuous learning, certifications and career growth in modern cloud-native technologies.

About the Role

This role owns the design and delivery of AWS-based data engineering solutions that support enterprise analytics and AI/ML workloads. The focus is on building dependable pipelines, scalable storage layers and efficient processing systems that can handle large data volumes without sacrificing quality or performance. You will work across ingestion, transformation, orchestration, governance and production support to ensure data products are accurate, secure and ready for downstream business use. Success in this role means turning platform requirements into stable, reusable and well-performing data assets that improve reporting speed, analytical depth and operational resilience. The position calls for strong hands-on engineering, disciplined troubleshooting and the ability to work in a cloud-native environment with modern tooling and delivery practices.

Key Responsibilities

• Build AWS-native data pipelines and ingestion frameworks that move data reliably from source systems into analytics-ready environments, with clear controls for traceability, scale and production stability.
• Develop ETL and ELT workflows using AWS services so business teams receive timely, trusted data for reporting, analysis and AI/ML use cases.
• Design and maintain scalable data lake solutions that support structured and semi-structured data, while keeping access, lifecycle and governance requirements aligned.
• Implement Spark-based data processing workflows that efficiently transform large datasets and reduce bottlenecks in distributed processing environments.
• Maintain data quality, governance and security standards so the platform remains trustworthy, compliant and usable for enterprise decision-making.
• Support AI/ML data preparation and feature engineering by shaping reliable datasets that improve model readiness and downstream analytical value.
• Optimize large-scale workloads and troubleshoot production issues quickly so platform performance, reliability and business continuity remain strong.

Essential Skills & Technologies

• Strong hands-on experience with AWS data services including S3, Glue, Lambda, EMR, Redshift, Athena and Lake Formation, with the ability to apply them in end-to-end platform solutions.
• Strong Python, PySpark and SQL skills for building transformation logic, automation and scalable data processing routines across modern pipelines.
• Experience with Spark and distributed data processing so large datasets can be handled efficiently and performance issues can be identified and resolved.
• Knowledge of Terraform or CloudFormation and CI/CD practices to support repeatable infrastructure delivery and controlled application changes.
• Experience with modern data platform architectures, including data lake patterns, ingestion frameworks and enterprise analytics integration.
• Understanding of data lake architecture, infrastructure as code and DevOps practices to improve reliability, maintainability and delivery speed.
• Familiarity with performance optimization and enterprise analytics platforms such as Power BI is a plus when supporting downstream consumption needs.

Additional Plus

• Exposure to enterprise-scale cloud, AI and analytics transformation projects is highly valuable because the work sits close to large business and technology change initiatives.
• Experience with Power BI or similar enterprise analytics platforms can help connect engineering output to business reporting and analysis needs.
• Background in DevOps, automation and platform performance tuning will strengthen your ability to support stable, efficient delivery.

What You'll Bring

• 3–9 years of relevant experience in AWS data engineering, with demonstrated ownership of pipelines, data lake components and production support in cloud environments.
• A strong engineering mindset with the ability to balance scalability, reliability and governance while delivering practical outcomes for analytics and AI/ML teams.
• Clear hands-on capability in Python, PySpark, SQL and core AWS services, paired with structured troubleshooting and strong execution in distributed systems.
• The discipline to work across technical and business stakeholders, translating data needs into maintainable platform solutions that drive measurable value.

Why Join Us

You will join a team working on meaningful cloud and analytics transformation work at enterprise scale, where good engineering directly improves business decision-making and operational performance. The role offers the chance to build modern AWS data platforms that support reporting, AI/ML and business intelligence in a high-impact environment. You will be part of a collaborative global delivery setup where technical quality, learning and growth are taken seriously. The work is well suited to someone who wants to deepen their expertise in cloud-native data engineering while contributing to solutions that are used by leading organizations. If you enjoy solving complex data problems, building reliable systems and working with modern cloud technologies, this role offers a strong platform for growth.

What We Offer

• Exposure to enterprise-scale cloud, AI and analytics transformation projects that create visible business impact.
• A collaborative global delivery environment that supports teamwork across data, cloud and analytics disciplines.
• Continuous learning, certifications and career growth opportunities in modern cloud-native technologies.
• Opportunity to work with modern AWS-based data engineering tools and architectures in production settings.