Job Description:

We help enterprises transform fragmented data, systems, documents, and business knowledge into trusted, governed, reusable, and AI-ready enterprise context. Our work spans modern data foundations, enterprise data architecture, integration, semantic layers, knowledge graphs, GraphRAG, ontologies, metadata, intelligent automation, and production-grade AI systems.

Role Overview:

- We are seeking a motivated Data Engineer with solid hands-on experience building and operating data pipelines, integrations, and models in production environments.

- This is a hands-on engineering role. You will build, test, and maintain the data pipelines, transformations, and data products that underpin our clients' data platforms, working from designs set by senior architects.

Key Responsibilities:

- Build and maintain ingestion, integration, transformation, and orchestration pipelines.

- Implement ETL/ELT, API, CDC, and streaming integration patterns under architectural guidance.

- Develop and maintain logical and physical data models.

- Build and support data products, including documentation of schemas and data contracts.

- Write clean, tested, well-documented, production-grade code.

- Implement metadata, lineage, data quality checks, and monitoring for pipelines you own.

- Support analytics, ML, and GenAI use cases by delivering reliable, well-structured data.

Required Experience and Skills:

- 3 - 6 years of experience in data engineering or a closely related role.

- Strong hands-on coding ability (Python and/or SQL) for building data pipelines.

- Practical experience with ETL/ELT tools and workflow orchestration (e.g., Airflow, dbt, or similar).

- Working knowledge of at least one major cloud data platform (Snowflake, Databricks, AWS, Azure, or GCP).

- Understanding of data modeling fundamentals (dimensional modeling, normalization).

AI and Modern Engineering Exposure (Good to Have):

- Familiarity with LLM/GenAI concepts and basic RAG pipelines.

- Exposure to vector databases or knowledge graphs.

- Experience using AI-assisted coding tools such as Claude Code, Cursor, or GitHub Copilot.

Education:

- Bachelor's degree in Computer Science, Engineering, or a related field.