Data Engineer
Delhi NCR, IndiaRemoteFull-timeSenior · 6-8 years
About this role
Role: Full Stack Developer
Location: India, Remote
Experience: 6-8 Years
Algoworks
About the company
Algoworks is an award-winning artificial intelligence, engineering services and experience transformation firm with offices across the United States, Europe, South America and India. We bring together a global team of engineers, architects, designers, researchers and operators united by rigor, accountability and a commitment to delivering measurable results.
For over 20 years, Algoworks has partnered with Fortune 500 organizations across the Americas, Europe and Asia to define, build and run technology that drives meaningful business outcomes. Our work combines human-centered design, engineering excellence and AI-powered capabilities to solve complex challenges with clarity and precision. Innovation, particularly in the responsible application of AI, is embedded in how teams approach problem-solving and continuous improvement.
At Algoworks, growth is continuous and closely tied to impact. Teams collaborate across geographies and disciplines, strengthening outcomes through shared insight and collective expertise. The culture values transparency, open dialogue and an environment where every voice is heard and contribution is recognized.
Through collaboration, accountability and a focus on results, Algoworks operates at the intersection of technology and people, building not only advanced systems but strong global teams that elevate performance and create lasting impact.
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Role overview
We are seeking a Data Engineer with strong experience in Snowflake, Dagster, dbt, Python, PySpark, GraphQL and SQL to build reliable, scalable and well-governed data platforms.
You will design data pipelines, transformation models and integration services that enable trusted analytics and efficient access to business data across the organization.
Key responsibilities:
1.Data pipeline development
- Design, develop and maintain orchestrated data pipelines using Dagster, Python and PySpark.
- Build modular, tested and documented transformation models using dbt.
- Develop and optimize Snowflake data models, SQL queries and warehouse workloads.
2.Data quality and integration
- Implement data-quality checks, observability, lineage and failure-handling mechanisms across pipelines.
- Integrate data sources and downstream consumers through APIs, including GraphQL-based interfaces where required.
3.Collaboration
- Work with analytics, product and engineering teams to translate business requirements into dependable data solutions.
- Contribute to code reviews, documentation, deployment pipelines and production support processes.
Required technical skills and competencies:
- 3+ years of data engineering experience with cloud data platforms.
- Strong hands-on experience with Snowflake, Dagster and dbt.
- Advanced Python and SQL skills with experience building production-grade pipelines.
- Working knowledge of PySpark for distributed data processing.
- Experience with data modelling, orchestration, testing and performance optimization.
Must have skills:
- Strong Snowflake, dbt and SQL development experience.
- Hands-on pipeline orchestration using Dagster.
- Solid Python and PySpark programming skills.
- Ability to build reliable, tested and maintainable data workflows.
Good to have skills:
- Experience designing GraphQL APIs or integrating GraphQL data sources.
- Knowledge of data governance, lineage, observability and metadata management.
- Exposure to cloud services, containerization and infrastructure-as-code.
- Experience with CI/CD practices for data pipelines and dbt projects.
- Hands-on experience using AI-assisted development tools is an advantage.
Desired attributes:
- Strong ownership mindset and disciplined engineering approach.
- Excellent analytical and troubleshooting skills.
- Effective communicator who collaborates well with technical and business stakeholders.
- High attention to data quality, reliability and operational excellence.
- Passion for continuous improvement and scalable data engineering practices.
Interview process
2 rounds of discussion.