Senior Manager, Data Engineering
New
United StatesFull-TimeManager
Salary133,200 - 195,000 USD per year
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Job Details
- Experience
- 10+ years
- Required Skills
- AWSPythonSQLETLSnowflakeCI/CDTerraformData modelingDatabricksPySpark
Requirements
- Bachelor’s degree in Computer Science, Information Systems, Engineering, Mathematics, or related field.
- 10+ years of progressive experience in data engineering, analytics engineering, business intelligence, or related technical roles.
- Advanced proficiency in SQL and Python.
- Hands-on experience building and maintaining scalable ETL/ELT pipelines and distributed data processing solutions using PySpark.
- Deep understanding of enterprise data modeling, data warehouses, data lakes, and modern lakehouse architectures.
- Experience implementing data governance, data quality, monitoring, and security frameworks.
- Experience with cloud platforms including AWS services (S3, IAM, Glue, Lake Formation), Snowflake, Databricks, Azure, or GCP.
- Knowledge of cloud security, identity management, access controls, and secure architecture practices.
- Experience with Terraform or similar Infrastructure-as-Code tools, Git-based development workflows, and CI/CD pipelines.
- Strong leadership skills with the ability to influence technical decisions and guide complex engineering initiatives.
- Experience working in Agile environments such as Scrum, Kanban, or SAFe.
Responsibilities
- Lead the design, development, and optimization of modern enterprise data platforms supporting analytics, reporting, machine learning, and AI initiatives.
- Architect and implement scalable data solutions, including ETL/ELT pipelines, data models, lakehouse architectures, and cloud-based data platforms.
- Translate business requirements into secure, reliable, and high-performing data products that deliver measurable business value.
- Partner with data scientists, analysts, architects, engineers, and business leaders to define technical strategies and deliver innovative solutions.
- Build and maintain cloud-native data architectures across platforms such as AWS, Snowflake, Azure, Databricks, and related technologies.
- Implement data quality frameworks, monitoring solutions, governance practices, security controls, and platform optimization strategies.
- Automate infrastructure provisioning and deployment processes using Infrastructure-as-Code tools and DevOps best practices.
- Drive technical initiatives, influence architectural decisions, and establish engineering standards across data teams.
- Mentor data engineers, conduct code reviews, provide technical guidance, and promote continuous learning and improvement.
- Evaluate emerging technologies, including generative AI, machine learning platforms, and automation solutions, to improve data capabilities.
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