This role does not offer sponsorship and is open to candidates authorized to work in the U.S. without sponsorship (USC/GC).
Responsibilities
Design and enhance enterprise-grade data platforms, including ingestion, transformation, storage, orchestration, and data serving layers for both batch and streaming use cases
Build and maintain scalable data pipelines, reusable frameworks, and enterprise data models to support analytics, artificial intelligence, and operational reporting
Define and manage semantic data layers while implementing governance controls such as data quality validation, lineage tracking, metadata management, and secure access
Establish engineering standards for development, testing, version control, documentation, and continuous integration and delivery practices
Optimize data solutions for cost efficiency, scalability, and performance using modern engineering and operational practices
Lead technical design reviews, incident response activities, and root cause analysis to improve platform stability and reliability
Collaborate with data science teams to deploy and operationalize machine learning models for batch and real-time use cases
Partner with cross-functional teams including analytics, security, and architecture to deliver compliant, high-quality data solutions
Evaluate new technologies and guide architectural decisions, including build-versus-buy considerations
Mentor engineering teams through technical guidance, code reviews, and knowledge sharing to raise overall engineering standards
Promote consistency and reuse across distributed teams by sharing best practices and standardized components
Required Experience and Skills
Bachelor’s degree in computer science, statistics, applied mathematics, or a related quantitative field
At least 8 years of experience in data engineering or data platform development, including significant experience in senior or principal-level roles
Strong proficiency in SQL, Python, and large-scale data processing frameworks such as Apache Spark or PySpark
Hands-on experience with major cloud platforms such as AWS, Azure, or Google Cloud, along with modern data platforms such as Databricks or Snowflake
Experience with streaming technologies such as Apache Kafka or similar tools and orchestration frameworks such as Apache Airflow
Strong background in data modeling, including dimensional, data vault, and domain-oriented approaches
Experience implementing data governance frameworks, including quality controls, lineage tracking, metadata management, and access controls
Knowledge of software engineering practices including CI/CD, infrastructure as code, and automated testing
Proven ability to lead complex technical initiatives, influence stakeholders, and guide engineering teams
Strong communication skills with the ability to present technical concepts clearly and effectively
Ability to manage multiple priorities and work effectively in fast-paced environments