Senior Lead Data Engineer
New
S
StrykerData Engineering
United StatesFull-TimeLead
Salary118,000 - 255,700 USD per year
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Job Details
- Experience
- 6+ years
- Required Skills
- PythonSQLAzureSparkCI/CDDatabricksAzure DevOps
Requirements
- Bachelor’s degree in Computer Science, Data Engineering, Data Science, Information Systems, Mathematics, Statistics, or a related technical field.
- 6+ years of experience in data engineering, analytics engineering, software engineering, or enterprise data platform development.
- Proven experience architecting, building, and modernizing scalable cloud-based data platforms in an enterprise environment.
- Hands-on experience with Azure-based data engineering technologies, including Databricks, Delta Lake, Azure Data Factory, Azure DevOps, CI/CD pipelines, and infrastructure automation.
- Strong proficiency in SQL and Python, with experience using Spark or similar distributed data processing frameworks.
- Experience designing reliable ETL/ELT pipelines, data warehouse or Lakehouse architectures, data models, and performance-optimized analytics solutions.
- Experience integrating data from multiple ERP or enterprise source systems and harmonizing inconsistent master, supplier, purchasing, or transactional data into common data models.
- Experience with API integrations, REST services, authentication, security, data governance, and production support practices.
- Demonstrated ability to establish engineering standards, conduct architecture reviews, improve documentation, mentor engineers, and influence technical direction without direct authority.
- Ability to partner with analysts, engineers, architects, and business stakeholders to translate complex business requirements into scalable, maintainable engineering solutions.
Responsibilities
- Lead the architecture, development, and modernization of scalable enterprise data platforms that support global procurement analytics and business transformation.
- Define and help execute a multi-year data engineering strategy focused on platform scalability, reliability, automation, technical debt reduction, and long-term maintainability.
- Design, build, and optimize Azure-based data solutions using technologies such as Databricks, Delta Lake, Azure Data Factory, Azure DevOps, CI/CD pipelines, and infrastructure automation.
- Integrate and harmonize data across multiple ERP systems by standardizing supplier, purchasing, and master data into common enterprise data models.
- Partner with procurement analysts, architects, engineers, and business stakeholders to translate complex business needs into reusable, scalable data products and engineering solutions.
- Establish engineering standards, conduct architecture reviews, improve documentation, and mentor engineers to raise the overall technical capability of the team.
- Identify and implement AI-enabled approaches that accelerate development, improve data quality, automate documentation, support testing, and enhance analyst productivity.
- Evaluate and recommend tools, frameworks, patterns, and platform investments that improve performance, reliability, security, governance, and operational efficiency.
- Support the development of internal data products, APIs, and user-facing applications that simplify access to procurement insights and improve analyst productivity.
- Present technical recommendations, roadmap priorities, tradeoffs, and business impacts to technical and non-technical stakeholders, including leadership teams.
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