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Tier-1 brand, remote-friendly senior data role in metro with broad dbt/cloud requirements drives high candidate density.
dbt/cloud data engineering skills are broadly transferable across industries.
Explicit 8–12 years and mandatory dbt production experience plus specific cloud/data tech creates high shortlisting strictness.
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Own the design, implementation, and optimization of scalable data pipelines and ELT architectures using dbt and cloud data warehouse technologies (e.g., Snowflake, Redshift, BigQuery).
Lead end-to-end dbt project cycles including environment management, reusable macro creation, and performance tuning in production.
Implement monitoring, logging, alerting, and observability for data pipelines and dbt transformations to ensure robustness and high data volume handling.
8–12 years of total IT experience with a minimum of 6 years focused on data warehouse, data lakes, ETL/ELT, and cloud data pipelines.
Proven hands-on experience with at least one major cloud provider (AWS, Azure, or GCP) and related data services such as Snowflake, BigQuery, Redshift, ADLS, or S3.
Strong expertise with dbt, including at least one full end-to-end production implementation cycle.
Proficient in Python and cloud serverless orchestration components (e.g. AWS Lambda, Azure Functions) and familiar with cloud DevOps practices including CI/CD and infra-as-code.
Experienced in designing scalable, reusable data transformation frameworks and layered data models using dbt in enterprise-grade cloud environments.
Capable of leading architectural and technical discussions on cloud data solutions, focusing on cost optimization, performance tuning, and best practices adoption.
Comfortable integrating dbt with orchestration and data integration tools such as Airflow, Azure Data Factory, or dbt Cloud, and skilled in troubleshooting complex data workflows.