Role & Responsibilities:

Primary Responsibilities:

1. Data Architecture & Canonical Model Design:

- Design, build, and maintain canonical data models that serve as the single source of truth across analytics and AI use cases.

- Define and enforce data contracts between upstream systems and downstream consumers.

- Handle schema evolution, versioning, and drift management proactively.

- Ensure alignment between business semantics and physical data models.

2. Data Engineering & Pipeline Development:

- Build scalable and efficient data pipelines using Snowflake, SQL, and Python.

- Process both structured and semi-structured data (JSON, logs, API payloads).

- Optimize transformations for performance, cost, and scalability.

- Implement reusable, modular pipeline components.

3. Advanced Data Modeling for Analytics:

- Design dimensional and normalized data models for reporting, ML, and AI workloads.

- Optimize data models for BI tools, self-service analytics, and LLM consumption.

- Develop metric-layer ready models to ensure consistency across reporting.

4. Data Governance & Quality:

- Implement data validation, monitoring, and quality checks across pipelines.

- Build frameworks to detect schema drift and data inconsistencies.

- Ensure adherence to data governance, lineage, and auditability standards.

- Support compliance requirements (PHI/PII handling, access control, traceability).

5. AI/ML & GenAI Enablement:

- Structure data to support RAG pipelines, embeddings, and LLM-based applications.

- Enable feature-ready datasets for ML and AI use cases.

- Collaborate with AI/ML engineers to ensure data readiness for agentic workflows.

6. Performance Optimization & Platform Engineering:

- Optimize Snowflake performance (clustering, partitioning, query tuning, cost management).

- Build frameworks for data observability, monitoring, and alerting.

- Improve pipeline reliability, scalability, and fault tolerance.

Preferred Candidate Profile:

- Bachelors degree in Computer Science, Engineering, Data Engineering, or a related technical field (or equivalent practical experience).

- Proven expertise in with Snowflakes and Databricks.

- 12+ years of overall experience in software engineering and data engineering roles, with significant experience designing and delivering large scale data platforms in enterprise environments.

- Solid hands on experience with cloud based data platforms (Azure and/or GCP), including data storage, processing, orchestration, and monitoring services.

- Deep experience with ETL/ELT frameworks, batch and streaming data processing, and distributed data systems.

- Experience collaborating with Analytics, BI, Data Science, and Product teams to deliver trusted, reusable, and performant data assets.

- Proven expertise in data engineering architecture and solution design, including building, optimizing, and scaling high volume, high availability data pipelines.

- Advanced proficiency in SQL and at least one programming language such as Python for data pipeline and platform development.

- Solid knowledge of data quality, data observability, lineage, and metadata management, and implementing governance controls in enterprise data ecosystems.

- Demonstrated ability to work across cloud and on prem ecosystems, supporting hybrid data architectures at scale.