Data Engineer

Designing and building scalable data pipelines for ingesting and processing structured and unstructured data from multiple sources

Implementing ETL/ELT processes and orchestration workflows to transform raw data into analytical formats

Architecting and managing data warehouses and data lakes with focus on storage optimization, retrieval performance, and lifecycle management

Writing production-quality SQL and Python code for data processing and transformation

Establishing and monitoring data quality frameworks including testing, validation, and observability systems

Designing and optimizing data models including dimensional modeling, star schemas, and appropriate normalization strategies

Designing data governance, access controls, and security measures including PII handling and compliance standards

Building and maintaining dashboards, reporting systems, and semantic layers for business intelligence and analytics

Automating manual data processes and optimizing workflows for performance and cost efficiency

Building observability and monitoring infrastructure for data pipelines including alerting on anomalies and data drift

Implementing CI/CD practices and IT General Controls to ensure reliability and security of data flows

Building data products and metrics that power machine learning training pipelines and model improvement

Designing privacy-first data architectures and implementing controls for sensitive employee and user data

Designing data infrastructure for AI/ML workloads including training data preparation and quality evaluation

Implementing master data management patterns including golden records, deduplication, and identity resolution

Designing and operating medallion architecture patterns for structured data transformation (bronze/silver/gold)

Building streaming data pipelines and real-time data processing systems

Managing reverse ETL and data activation to push insights back into operational systems

Collaborating with cross-functional stakeholders including business teams, analysts, data scientists, and engineers to translate requirements into data solutions

Translating ambiguous business requirements into scalable datasets, metrics, and self-serve data products

Communicating technical concepts clearly through documentation, runbooks, and knowledge sharing with team members

Mentoring team members and establishing best practices, standards, and code review processes