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Senior role with niche Snowflake/dbt skills but remote nationwide reach creates medium competition.
Core data engineering skills are transferable, but Snowflake/dbt and finance context raise domain specificity to medium.
Explicit 12+ years plus mandatory Snowflake/dbt, AWS, Kafka and data governance make shortlisting highly strict.
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Lead design, development, and implementation of scalable batch and real-time data pipelines using Snowflake, Amazon Redshift, and AWS services.
Own end-to-end data engineering initiatives including data ingestion, transformation (ETL/ELT), quality, and delivery across enterprise platforms with modern tools like dbt and Apache Airflow.
Drive best practices in data modeling, governance, observability, CI/CD, mentoring teams, and enabling AI/ML data initiatives impacting enterprise-wide data platforms.
Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field.
12+ years of experience in Data Engineering, Data Warehousing, and Software Development.
Hands-on expertise with Snowflake, Amazon Redshift, AWS cloud services (S3, Lambda, Glue, IAM, ECS, CloudFormation), and modern data transformation frameworks such as dbt.
Experience with orchestration tools (Apache Airflow), streaming/CDC platforms (Kafka, STRIIM), observability tools (Datadog), and CI/CD tooling (GitHub Actions, Jenkins, Terraform).
Senior-level technical leader with proven ability to lead and mentor data engineering teams in large-scale cloud native environments.
Experienced in building enterprise-scale data platforms integrating batch, real-time streaming and ELT frameworks aligned with governance and security.
Hands-on expertise balancing solution architecture and operational responsibilities, collaborating cross-functionally with data architects, analysts, and product teams.