Job Description
Snowflake Data Engineer, Senior
Department: Information Technology
Employment Type: Full Time
Location: Hyderabad
We are looking for a technically sharp, solution-oriented Senior Data Engineer with deep expertise in Snowflake to join our growing data team. This role is ideal for someone who can operate as a hands-on implementation partne—working closely with business and technical stakeholders to deliver end-to-end data solutions from discovery and design through implementation, enablement, and optimization.
In this role, you will lead the design and development of scalable Snowflake-based data solutions, help drive AI readiness across the data platform, and enable capabilities such as semantic layer development, governed data access, metadata/catalog readiness, and AI-assisted engineering workflows You will play a key role in modernizing the data ecosystem, contributing to technical direction, and ensuring the platform is ready to support analytics, automation, and AI use cases.
The ideal candidate combines strong technical depth in Snowflake with excellent communication and presentation skills, a high degree of ownership, and a strong motivation to contribute beyond execution—bringing ideas, influencing direction, and helping teams and stakeholders move faster with confidence.
What We’re Looking For
We’re looking for a senior engineer who is more than just technically strong—we want someone who can own delivery end to end, work closely with stakeholders, and help translate business needs into scalable Snowflake solutions. This person should be comfortable operating as a hands-on builder, trusted technical partner, and enabler of AI readiness across the data platform.
The ideal candidate is:
- Deeply experienced in Snowflake and passionate about building modern cloud data solutions
- Comfortable acting as a forward-deployed engineer who can embed into initiatives and help move them from idea to production
- Strong in communication, presentation, and stakeholder engagement
- Motivated by contribution, continuous improvement, and solving meaningful business problems
- Knowledgeable about semantic layers, metadata/catalog readiness, data contracts, governed AI access, and AI-assisted engineering
- A collaborative mentor who raises engineering standards while remaining pragmatic and delivery-focused
A Typical Day in the Life Includes:
Data Platform & Engineering
- Design, build, and optimize scalable, secure, and high-performing data solutions in Snowflake including ingestion, transformation, modeling, and consumption layers.
- Develop and maintain robust ELT/ETL pipelines, data workflows, and transformation frameworks to support reporting, analytics, operational use cases, and AI initiatives.
- Build and optimize Snowflake data models using best practices for performance, maintainability, scalability, and cost efficiency.
- Ensure strong data quality, reliability, observability, security, and governance through testing, monitoring, documentation, and operational best practices.
- Drive continuous improvement in data engineering practices including CI/CD, code reviews, reusable frameworks, automation, and production support readiness.
Semantic Layer & AI Readiness
- Lead the implementation of semantic layer foundations using tools such as Snowflake Cortex Analyst YAML, dbt Metrics, or similar semantic modeling frameworks that make business data easier to discover, understand, and consume.
- Help prepare the data platform for AI readiness, including enabling structured, governed, and well-documented data assets that support AI/ML, copilots, intelligent agents, and natural language data experiences.
- Support data contracts and data product design practices that ensure upstream/downstream data reliability and enable scalable AI-ready data sharing across teams.
- Leverage Snowflake-native capabilities including Horizon Catalog, Cortex-powered workflows, streams, tasks, and dynamic tables for metadata visibility, cataloging, lineage, and observability.
- Contribute to enablement of modern tooling such as dbt, MCP-style integration patterns, Kiro, and Copilot/AI-assisted engineering practices.
Stakeholder Engagement & Delivery
- Act as an embedded engineering partner—engaging directly with projects end to end, from requirements clarification, data assessment, and design through build, deployment, testing, and post-production support.
- Partner closely with business stakeholders, analysts, data scientists, architects, and cross-functional engineering teams to shape and deliver solutions.
- Translate technical concepts clearly for both technical and non-technical audiences; confidently present designs, recommendations, trade-offs, and progress updates to senior leadership.
- Mentor junior and mid-level engineers, promote engineering standards, and contribute to a culture of continuous learning, collaboration, and delivery excellence.
Basic Qualifications:
-
5–8+ years of experience in data engineering, analytics engineering, or a related technical role, with strong exposure to modern cloud data platforms.
- Strong hands-on expertise in Snowflake as a core data platform, including:
- Data ingestion and loading patterns
- ELT/ETL design and implementation
- Performance tuning and query optimization
- Virtual warehouse sizing and workload management
- Cost optimization and storage/compute efficiency
- Secure data sharing and access control
- Streams, tasks, dynamic tables, and Snowflake-native features
- Strong proficiency in SQL and Python for data transformation, automation, and engineering workflows.
- Solid experience designing and implementing scalable data models, including dimensional modeling, business-friendly consumption models, and semantic-ready structures.
- Experience building production-grade pipelines and data solutions with strong focus on reliability, observability, and maintainability
- Strong understanding of data warehousing concepts, medallion/layered architecture, metadata-driven development, and best practices for analytics-ready data.
- Experience supporting or enabling AI/ML or AI-readiness initiatives, including preparing trusted, governed, and well-structured data for downstream intelligent use cases.
- Strong stakeholder engagement skills, with the ability to gather requirements, shape solutions, and work effectively across technical and business teams.
- Excellent communication, facilitation, and presentation skills; able to explain complex technical topics clearly and influence decision-making.
- Strong ownership mindset with the ability to independently drive workstreams and deliver outcomes across the full project lifecycle.
- Contribution-motivated team player who actively looks for opportunities to improve the platform, reduce friction, and create business value.
- Strong ownership mindset with ability to independently drive workstreams and deliver outcomes across the full project lifecycle.
Preferred Qualifications:
- Hands-on experience with dbt (data build tool) for transformation, testing, documentation, and modular analytics engineering practices.
- Experience with orchestration tools such as Airflow or similar workflow schedulers.
- Familiarity with Snowflake AI and governance capabilities, such as:
- Snowflake Cortex / Cortex-powered development or code assistance
- Snowflake Horizon Catalog
- Semantic layer or metadata-driven data enablement
- Governance and discoverability capabilities for AI-ready data
- Exposure to MCP-style integration patterns, modern data product design, or emerging AI/agent-enablement frameworks.
- Experience with data contracts and data product design to support reliable, governed data sharing across teams and AI consumers.
- Experience using Copilot, Kiro, or similar AI-assisted engineering tools to improve productivity, code quality, documentation, and delivery speed.
- Experience with CI/CD pipelines, Git-based development workflows, and release management for data engineering assets.
- Strong knowledge of data governance, lineage, cataloging, security, and compliance best practices in enterprise environments.
- Experience working directly with business programs or product teams in a forward-deployed engineering or embedded delivery model.
- Experience contributing to architecture discussions, technical roadmaps, and platform modernization initiatives.
