Data Engineer

About the Role

As a Data Engineer, you will act as the bridge between raw data sources and actionable business intelligence. You will develop robust batch and real-time data pipelines, contribute to modern data architectures, and build data platforms. You will work closely with data scientists, analysts, product teams, and other engineering teams to build high-quality data products and platforms. You will take end-to-end ownership of your solutions, ensuring their reliability, scalability, performance, and cost efficiency.

Responsibilities

• Design, build, and maintain scalable batch and real-time data pipelines and processing systems.
• Design, build, and evolve cloud-native data platforms that enable data ingestion, processing, analytics, and observability across the organization.
• Build APIs and data services to expose processed data to downstream applications and teams.
• Develop data models and warehousing solutions that support analytics, reporting, and downstream applications.
• Collaborate with data scientists, analysts, product teams, and other engineering teams to deliver trusted data products.
• Monitor, optimize, and maintain data pipelines and processing systems to ensure high performance, reliability, scalability, and cost efficiency.
• Take end-to-end ownership of data services and pipelines, including deployment, monitoring, troubleshooting, and incident resolution.

Expected Qualifications

• Bachelor's degree in Computer Science, Engineering, MIS, Mathematics, or a related field.
• 2+ years of experience as a Data Engineer, Data Developer, or in a similar data-focused role.
• Solid understanding of distributed systems, parallel processing, and fault-tolerant architectures.
• Strong software engineering skills in at least one of Python, Scala, or Java (Go is a plus).
• Hands-on experience with ETL/ELT processes and big data processing frameworks such as Spark or Flink.
• Advanced SQL skills and experience with structured and semi-structured data formats.
• Strong understanding of data modeling concepts and analytical data structures.
• Experience in developing cloud-native data platforms and modern data architectures, including data lakes, data warehouses, and lakehouses.
• Experience with streaming technologies, Change Data Capture (CDC), and workflow orchestration.
• Familiarity with AI/ML data infrastructure, LLM-based applications, RAG architectures, and semantic grounding for AI agents.
• Experience with Docker, Kubernetes, CI/CD, and Infrastructure as Code.
• Experience with automated testing, data quality validation, monitoring, and observability practices.
• Strong problem-solving skills and clear written and verbal communication in English.