Job Summary

Lead the design, development, and optimization of scalable cloud-based data engineering solutions using AWS and Snowflake. The role focuses on building reliable ETL/ELT pipelines, modern data warehouse solutions, data models, and analytics-ready datasets. The ideal candidate will combine strong hands-on technical expertise with stakeholder collaboration, technical leadership, mentoring, and a focus on data quality, security, performance, and cost optimization.

Roles and Responsibilities

Data Pipeline Development: Design, develop, and maintain scalable and fault-tolerant batch and real-time data pipelines using AWS and Snowflake. Build robust ETL/ELT workflows for processing structured and unstructured data.
Data Warehousing & Modeling: Design and optimize Snowflake data warehouses and implement data models using Dimensional Modeling, Star Schema, Snowflake Schema, and Data Vault methodologies. Leverage Snowflake capabilities such as Snowpipe, Streams, Tasks, Time Travel, clustering, and materialized views.
AWS Cloud Engineering: Develop cloud-based data solutions using Amazon S3, AWS Glue, Lambda, EMR, Athena, Step Functions, EC2, and IAM, ensuring scalability, reliability, security, and operational efficiency.
Performance & Cost Optimization: Optimize Snowflake virtual warehouses, SQL queries, data processing workloads, and storage utilization. Implement query tuning and cloud cost-optimization strategies.
Data Governance & Security: Implement data quality, governance, security, and access-control best practices across AWS and Snowflake environments. Manage RBAC, permissions, data integrity, and compliance requirements.
Stakeholder Collaboration: Work closely with Data Scientists, Data Analysts, Product Managers, and business stakeholders to understand requirements and deliver reliable, analytics-ready datasets and scalable data solutions.
Technical Leadership & Mentoring: Mentor junior data engineers, conduct code reviews, promote best practices in development and testing, and contribute to technical design decisions and continuous improvement initiatives.
CI/CD & Engineering Practices: Implement automated testing, version control, CI/CD practices, data quality checks, and reliable deployment processes using modern data engineering frameworks.

Required Skills

AWS, Snowflake, SQL, Python, ETL/ELT, Data Warehousing, Data Modeling, Apache Airflow, AWS Glue, Snowpipe, Streams, Tasks, Data Quality, CI/CD, Git, and Cloud Data Engineering.
5–8 years of hands-on experience in Data Engineering, Data Warehousing, or Data Architecture.
Strong expertise in Snowflake architecture, query optimization, virtual warehouses, resource monitors, clustering, materialized views, Snowpipe, Streams, and Tasks.
Hands-on experience with AWS S3, EC2, Lambda, Glue, Athena, Step Functions, IAM, with exposure to EMR preferred.
Advanced SQL skills including complex joins, CTEs, window functions, analytical functions, and query tuning.
Strong Python programming skills for data processing, API integration, automation, and scripting.
Good understanding of Kimball methodology, Dimensional Modeling, Data Vault, Star Schema, and Snowflake Schema.
Experience with Apache Airflow, AWS Step Functions, or similar workflow orchestration tools.
Knowledge of data quality frameworks, automated testing, CI/CD, Git, security, governance, and cloud cost optimization.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status.

Job Summary

Roles and Responsibilities

Data Pipeline Development: Design, develop, and maintain scalable and fault-tolerant batch and real-time data pipelines using AWS and Snowflake. Build robust ETL/ELT workflows for processing structured and unstructured data.
Data Warehousing & Modeling: Design and optimize Snowflake data warehouses and implement data models using Dimensional Modeling, Star Schema, Snowflake Schema, and Data Vault methodologies. Leverage Snowflake capabilities such as Snowpipe, Streams, Tasks, Time Travel, clustering, and materialized views.
AWS Cloud Engineering: Develop cloud-based data solutions using Amazon S3, AWS Glue, Lambda, EMR, Athena, Step Functions, EC2, and IAM, ensuring scalability, reliability, security, and operational efficiency.
Performance & Cost Optimization: Optimize Snowflake virtual warehouses, SQL queries, data processing workloads, and storage utilization. Implement query tuning and cloud cost-optimization strategies.
Data Governance & Security: Implement data quality, governance, security, and access-control best practices across AWS and Snowflake environments. Manage RBAC, permissions, data integrity, and compliance requirements.
Stakeholder Collaboration: Work closely with Data Scientists, Data Analysts, Product Managers, and business stakeholders to understand requirements and deliver reliable, analytics-ready datasets and scalable data solutions.
Technical Leadership & Mentoring: Mentor junior data engineers, conduct code reviews, promote best practices in development and testing, and contribute to technical design decisions and continuous improvement initiatives.
CI/CD & Engineering Practices: Implement automated testing, version control, CI/CD practices, data quality checks, and reliable deployment processes using modern data engineering frameworks.

Required Skills

AWS, Snowflake, SQL, Python, ETL/ELT, Data Warehousing, Data Modeling, Apache Airflow, AWS Glue, Snowpipe, Streams, Tasks, Data Quality, CI/CD, Git, and Cloud Data Engineering.
5–8 years of hands-on experience in Data Engineering, Data Warehousing, or Data Architecture.
Strong expertise in Snowflake architecture, query optimization, virtual warehouses, resource monitors, clustering, materialized views, Snowpipe, Streams, and Tasks.
Hands-on experience with AWS S3, EC2, Lambda, Glue, Athena, Step Functions, IAM, with exposure to EMR preferred.
Advanced SQL skills including complex joins, CTEs, window functions, analytical functions, and query tuning.
Strong Python programming skills for data processing, API integration, automation, and scripting.
Good understanding of Kimball methodology, Dimensional Modeling, Data Vault, Star Schema, and Snowflake Schema.
Experience with Apache Airflow, AWS Step Functions, or similar workflow orchestration tools.
Knowledge of data quality frameworks, automated testing, CI/CD, Git, security, governance, and cloud cost optimization.

Assistant Manager - Data Engineering

What we know about this role

Skills required

Job description

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