Data Engineer

Kalpavruksh Systems; United States; Remote; USD 13,000 - 19,000; Full time

Job summary

Kalpavruksh Systems is seeking a Data Engineer to design and operate distributed data pipelines and data products in production environments. You will work with architects, AI engineers, and delivery leads to implement modern data platforms, integration flows, and AI-ready data foundations.

The role emphasizes hands-on coding (Python/SQL), ETL/ELT, and data modeling. You’ll contribute to standards, data contracts, and reliable data for analytics and GenAI use cases.

Qualifications

• 36 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).
• Exposure to data warehouses or lakehouses in a production setting.
• Basic understanding of data governance concepts: metadata, lineage, and data quality.
• Good communication skills and ability to document work clearly.
• Comfortable working independently on well-defined tasks and asking for guidance on ambiguous ones.

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.
• Troubleshoot pipeline issues and participate in performance tuning and optimization.
• Follow and help refine engineering standards and reusable patterns set by the architecture team.
• Participate in code reviews and pair with peers; take direction from senior architects on design decisions.

Skills

Python

SQL

ETL/ELT

Airflow

dbt

Snowflake

Databricks

Cloud platforms

Data modeling

Data governance

Documentation

Communication

Education

Bachelor's degree in CS/Engineering

Tools

Airflow

dbt

Snowflake

Databricks

AWS

Azure

GCP

Job description

Job Description

Title: Data Engineer

Location: Primary: Hyderabad, India. Other locations: Rest of India

Employment Type: Full-time, Remote

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.

We believe AI does not scale on data alone. It scales on trusted context.

Context66 combines deep architecture expertise, hands-on engineering, AI-native delivery, and reusable accelerators to help organizations simplify complexity, modernize platforms, improve data trust, and move AI from experimentation into measurable enterprise outcomes.

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.
• You will help implement modern data platforms, integration flows, and AI-ready data foundations, and increasingly take ownership of well-scoped components of the architecture as you grow.
• Work closely with senior data architects, other data engineers, AI engineers, and delivery leads.
• Contribute to technical standards, reusable assets, and code quality within the engineering team.

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.
• Troubleshoot pipeline issues and participate in performance tuning and optimization.
• Follow and help refine engineering standards and reusable patterns set by the architecture team.
• Participate in code reviews and pair with peers; take direction from senior architects on design decisions.

Required Experience and Skills

• 36 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).
• Exposure to data warehouses or lakehouses in a production setting.
• Basic understanding of data governance concepts: metadata, lineage, and data quality.
• Good communication skills and ability to document work clearly.
• Comfortable working independently on well-defined tasks and asking for guidance on ambiguous ones.

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.
• Interest in prompt engineering and workflow automation.

Mindset

• Eagerness to learn and grow into more architectural responsibility over time.
• Ownership of assigned tasks and reliable follow-through.
• Comfortable in a fast-paced, startup-like environment.
• Open to feedback and continuous improvement.
• Collaborative team player who communicates proactively.

Education

• Bachelor's degree in Computer Science, Engineering, or a related field.
• Relevant cloud/data certifications are a plus.