About the Role
We are looking for a skilled and passionate Data Engineer with deep expertise in dbt (data build tool) to help scale our modern data infrastructure. In this role, you will own the end-to-end data transformation layer, architect reliable ELT pipelines, and empower our analytics and product teams with clean, well-tested, and high-performance data models.

Key Responsibilities

- dbt Architecture & Development: Design, build, and maintain scalable dbt models, macros, snapshots, and packages to drive core business metrics and data transformations.
- Pipeline & Workflow Optimization: Own and optimize ELT workflows, collaborating with modern orchestration tools (e.g., Apache Airflow, Prefect, or Dagster) to ensure reliable data delivery.
- Data Quality & Governance: Implement automated data testing, data freshness checks, and documentation standards within dbt to guarantee high data reliability and traceability.
- Cloud Warehouse Management: Partner with stakeholders to optimize data warehouse performance (e.g., Snowflake, BigQuery, Databricks, or Redshift), focusing on query optimization, cost management, and compute sizing.
- CI/CD & Version Control: Evolve our CI/CD strategies for data codebases, managing git workflows, pull requests, and automated deployment pipelines for data models.




- Cross-Functional Collaboration: Act as a technical bridge between data analysts, data scientists, and software engineers to translate complex business logic into maintainable data pipelines. Required Qualifications & Skills
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Experience:
4+ years of professional experience in data engineering, analytics engineering, or software engineering.
- dbt Expertise: Proven, hands-on experience building, scaling, and maintaining production-grade dbt projects (Core or Cloud), including writing custom macros and utilizing packages.
- SQL & Data Modeling: Advanced SQL skills and a solid understanding of up-to-date data modeling methodologies (Dimensional Modeling, Data Vault, or Star Schema).
- Cloud Data Warehouses: Strong working knowledge of cloud-native data warehouses like Snowflake, Google BigQuery, or Amazon Redshift.. Preferred / Nice-to-Have Skills
- Certifications: dbt Fundamentals / Advanced Certifications or Cloud Data Engineer certifications (e.g., Snowflake SnowPro, Google Cloud Professional Data Engineer).
- Data Observability: Experience with data quality and observability platforms (e.g., Monte Carlo, Great Expectations, Elementary).
- Infrastructure as Code (IaC): Exposure to tools like Terraform or Docker.

📌 Data Engineer DBT (Noida)
🏢 Multiverse Solutions Private
📍 Noida