Senior Data Engineer
Bridgeway is seeking a Senior Data Engineer to design, develop, and maintain our data warehouse infrastructure. This role involves working closely with analysts, engineers, and other stakeholders to shape our data architecture, ensuring secure and efficient data pipelines, and enabling advanced analytics across the organization. The ideal candidate will have a strong background in data engineering, data warehousing, and ELT processes, along with a passion for optimizing data systems.
This is a remote position, with preference given to East Coast candidates.
Key Responsibilities:
Design, develop, and maintain a scalable data warehouse/lakehouse environment.
Design and implement ELT pipelines to ingest, transform, and deliver high-quality data for analytics and reporting, incorporating current best practices, such as “pipelines as code”.
Ensure data security and compliance, including role-based access controls for security, encryption, masking, and governance best practices to ensure compliant handling of sensitive information.
Optimize performance of data workflows and storage for cost efficiency and speed.
Partner with engineers, analysts, and stakeholders to meet data needs; balance cost, performance, simplicity, and time-to-value while mentoring teams and documenting standards.
Define and implement robust testing frameworks, enforce data contracts, and establish observability practices including lineage tracking, SLAs/SLOs, and incident response runbooks to maintain data integrity and trustworthiness.
Monitor, troubleshoot, and resolve data & automation issues.
Collaborate within an Agile-Scrum framework and develop comprehensive technical design documentation to ensure efficient and successful delivery.
Serve as a trusted expert on organizational data domains, processes, and best practices.
Requirements:
5+ years of experience in data engineering and ELT with a focus on large-scale data platforms
3+ years of experience with Databricks
Advanced proficiency in analytical SQL, including ANSI SQL, T-SQL, and Spark SQL
Strong Python skills for data engineering
Expertise in data modeling
Hands-on experience with data quality and observability practices (tests, contracts, lineage tracking, alerts)
Practical knowledge of orchestration tools and CI/CD concepts for data workflows
Excellent communication and a track record of technical leadership and mentoring
Strong understanding of integrating data solutions with AI and machine learning models
Strong problem-solving skills and attention to detail.
Experience with version control systems like Git preferred
Strong understanding of data governance and best practices in data management, with hands-on experience using Unity Catalog
Hands-on experience in designing and managing data pipelines using Delta Live Tables (DLT) on Databricks
Streaming and ingestion tools, such as Kafka, Kinesis, Event Hubs, Debezium, or Fivetran
DAX, LookML, dbt; Airflow/Dagster/Prefect, Terraform; Azure DevOps; Power BI/Looker/Tableau; GitHub CoPilot knowledge is a plus
Bachelor’s degree in Computer Science, Information Technology, or a related field. Master’s degree preferred
Apply Now
This is a remote position, with preference given to East Coast candidates.
Key Responsibilities:
Design, develop, and maintain a scalable data warehouse/lakehouse environment.
Design and implement ELT pipelines to ingest, transform, and deliver high-quality data for analytics and reporting, incorporating current best practices, such as “pipelines as code”.
Ensure data security and compliance, including role-based access controls for security, encryption, masking, and governance best practices to ensure compliant handling of sensitive information.
Optimize performance of data workflows and storage for cost efficiency and speed.
Partner with engineers, analysts, and stakeholders to meet data needs; balance cost, performance, simplicity, and time-to-value while mentoring teams and documenting standards.
Define and implement robust testing frameworks, enforce data contracts, and establish observability practices including lineage tracking, SLAs/SLOs, and incident response runbooks to maintain data integrity and trustworthiness.
Monitor, troubleshoot, and resolve data & automation issues.
Collaborate within an Agile-Scrum framework and develop comprehensive technical design documentation to ensure efficient and successful delivery.
Serve as a trusted expert on organizational data domains, processes, and best practices.
Requirements:
5+ years of experience in data engineering and ELT with a focus on large-scale data platforms
3+ years of experience with Databricks
Advanced proficiency in analytical SQL, including ANSI SQL, T-SQL, and Spark SQL
Strong Python skills for data engineering
Expertise in data modeling
Hands-on experience with data quality and observability practices (tests, contracts, lineage tracking, alerts)
Practical knowledge of orchestration tools and CI/CD concepts for data workflows
Excellent communication and a track record of technical leadership and mentoring
Strong understanding of integrating data solutions with AI and machine learning models
Strong problem-solving skills and attention to detail.
Experience with version control systems like Git preferred
Strong understanding of data governance and best practices in data management, with hands-on experience using Unity Catalog
Hands-on experience in designing and managing data pipelines using Delta Live Tables (DLT) on Databricks
Streaming and ingestion tools, such as Kafka, Kinesis, Event Hubs, Debezium, or Fivetran
DAX, LookML, dbt; Airflow/Dagster/Prefect, Terraform; Azure DevOps; Power BI/Looker/Tableau; GitHub CoPilot knowledge is a plus
Bachelor’s degree in Computer Science, Information Technology, or a related field. Master’s degree preferred
Apply Now