Senior Manager, Data Engineering
New
J
JobgetherData engineering
US; based in the United StatesFull-TimeManager
Salary135,000 - 175,000 USD per year
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Job Details
- Experience
- At least 6 years of experience in Data Engineering, Data Platform Engineering, Database Engineering, or a related technical field. At least 2 years of experience leading or managing Data Engineers or other technical teams.
- Required Skills
- AWSPostgreSQLSQLSnowflakedbt
Requirements
- At least 6 years of experience in Data Engineering, Data Platform Engineering, Database Engineering, or a related technical field.
- At least 2 years of experience leading or managing Data Engineers or other technical teams.
- Strong hands-on experience with Snowflake, dbt, SQL, and modern ETL/ELT architectures.
- Knowledge of PostgreSQL or comparable relational databases, including schema design, indexing, query optimization, and reliability.
- Experience with data ingestion and integration tools such as Fivetran, Meltano, or equivalent technologies.
- Understanding of dimensional modeling, Medallion architecture, and modern data warehouse design patterns.
- Knowledge of data quality, automated testing, observability, lineage, governance, security, and documentation practices.
- Experience operating cloud-based data platforms, including scalability, performance optimization, and cost management.
- Ability to work at architectural and implementation levels and solve technical problems hands-on.
- Leadership, mentoring, and team-development skills, including setting standards and empowering engineers.
- Ability to communicate technical concepts and recommendations to business and executive stakeholders.
- Experience with Python or Ruby for automation, tooling, or data processing is a plus.
Responsibilities
- Lead the Data Engineering team, establish technical direction, and develop a long-term roadmap aligned with business priorities.
- Own the architecture, reliability, performance, scalability, and evolution of Snowflake, PostgreSQL, and data pipelines.
- Lead the transition to a Medallion architecture and define standards for data ingestion, transformation, modeling, testing, documentation, and consumption.
- Manage and optimize the data stack, including Snowflake, dbt, Fivetran, Meltano, PostgreSQL, and AWS.
- Partner with application engineering teams on transactional database design, schema changes, query performance, scalability, and reliability.
- Establish engineering practices for data quality, automated testing, observability, lineage, governance, security, documentation, and incident response.
- Improve self-service data access and help teams discover, understand, and use trusted data.
- Monitor and optimize platform performance, infrastructure scalability, and operational costs.
- Lead, mentor, and develop Data Engineers, and contribute hands-on expertise to complex engineering problems.
- Collaborate with stakeholders to prioritize work and communicate technical decisions, risks, trade-offs, and investment needs.
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