AWS Data Engineer

Location: Bhubaneswar, Delhi-NCR, Remote Working | Experience: 3-5 years

About the Company

We are a technology-driven organization building reliable digital solutions and data platforms that help teams make faster, better decisions. Our work depends on strong engineering discipline, clear ownership, and the ability to turn complex data requirements into systems that are secure, scalable, and easy to operate.

We value people who think beyond delivery and focus on business impact, data quality, and long-term maintainability. The teams here work closely across engineering and stakeholders to design practical solutions, improve data flow, and support confident decision-making. This role sits in an environment where quality, accountability, and continuous improvement matter as much as speed.

About the Role

This role owns the design, build, and reliability of cloud-based data pipelines on AWS. You will work on moving data from source systems into trusted platforms that support analytics, reporting, and downstream applications.

The position is for someone who can translate business and technical needs into dependable data solutions, improve data availability and quality, and reduce operational friction through automation and disciplined engineering. Success means data consumers can trust what they use, teams spend less time fixing pipeline issues, and the platform scales with growing demand.

Key Responsibilities

• Build and maintain scalable data pipelines on AWS that move, transform, and validate data from multiple sources. The goal is to deliver trusted datasets that are ready for analytics and operational use.
• Design data models and storage patterns that improve performance, usability, and cost efficiency. Every design choice should strengthen reliability while keeping future growth manageable.
• Implement monitoring, logging, and alerting for data workflows so issues are detected early and resolved quickly. This reduces downtime and protects data confidence across teams.
• Work with stakeholders to understand data requirements and convert them into clear engineering deliverables. The outcome is better alignment between business needs and technical execution.
• Improve data quality, completeness, and consistency through validation rules and controlled processing logic. This ensures downstream users can rely on the data for decisions.
• Support automation, deployment, and operational readiness for data systems. The lever is repeatable engineering practices that lower manual effort and improve stability.
• Collaborate with engineering and analytics teams to troubleshoot production issues and optimize performance. The impact is faster resolution and better end-to-end system health.

Essential Skills & Technologies

• Strong hands-on experience with AWS data services such as S3, Glue, Lambda, Athena, Redshift, or similar tools. These are the core levers for building and operating cloud data platforms.
• Solid SQL, data modeling, and ETL/ELT development skills with an ability to handle structured and semi-structured data. This ensures data can be transformed into reliable business-ready outputs.
• Experience with scripting or programming in Python or similar languages for automation and pipeline development. This helps reduce manual work and improve engineering consistency.
• Understanding of orchestration, monitoring, and troubleshooting for data workflows. The focus is on keeping pipelines observable, stable, and easy to support.
• Working knowledge of version control, code review, and deployment practices. These habits improve quality, collaboration, and delivery speed.
• Ability to work with business and technical teams to clarify requirements and communicate progress. This keeps execution aligned with real business outcomes.

Additional Plus

• Exposure to streaming data, real-time processing, or event-driven architectures is a strong advantage. It helps if the platform needs faster data movement and near-real-time insights.
• Experience with infrastructure-as-code, containerization, or CI/CD practices for data workloads is a plus. These skills support cleaner operations and repeatable delivery.
• Familiarity with data governance, security, and access control in cloud environments can strengthen your fit. It helps protect sensitive data while enabling broader usage.

What You'll Bring

• You own data pipeline reliability and delivery, not just code output. Your work should make data more trustworthy, timely, and usable for the business.
• You know how to break down ambiguous data needs into practical AWS-based solutions. The result is faster execution with fewer handoffs and less rework.
• You care about operational excellence, including monitoring, quality checks, and root-cause analysis. That mindset keeps systems stable and users confident.
• You communicate clearly, collaborate well, and take accountability for outcomes. This helps engineering efforts stay aligned with business priorities.

Why Join Us

This is an opportunity to work on cloud data systems where your engineering decisions directly affect business visibility and decision-making. You will own meaningful problems around pipeline reliability, data quality, and scalable AWS architecture, rather than simply maintaining existing workflows.

You will collaborate with teams that value clarity, ownership, and practical problem-solving. The environment suits someone who wants to build dependable systems, improve how data moves across the organization, and see the direct impact of their work on operations and insights. If you enjoy turning complexity into simple, reliable data platforms, this role offers that scope.

What We Offer

• Competitive compensation aligned to experience and impact, with a range that supports strong AWS data engineering talent.
• A flexible working setup across Bhubaneswar, Delhi-NCR, and remote working options, designed to support productivity and balance.
• The chance to work on important data systems with real business visibility, where your contribution shapes reliability and decision quality.