Data Engineer

Communication: Communicates clearly and coordinates effectively with technical and business partners.

Data Security: Applies security practices such as encryption, anonymization, masking, and access- aware design.

Modern Data Architecture: Understands warehouse, lake, lake house, and cloud-based data architecture patterns.

Azure Familiarity: Understands Azure services used for data storage, integration, processing, orchestration, and security.

Orchestration: Manages pipeline orchestration, scheduling, monitoring, and data flow reliability.

Metadata Management: Uses metadata-driven practices to improve usability, lineage, and governance.

Version Control: Uses version control to support quality, traceability, and team collaboration.

Core Experience: 5+ years in SQL, data engineering, and data modeling.

Data Platforms: Builds and supports data warehouses, data lakes, and lake house environments.

Snowflake: Minimum 2 years of hands-on Snowflake experience.

Delivery Leadership: Leads full-lifecycle data engineering or reporting initiatives.

Data Ingestion: Designs and builds ingestion patterns for files, APIs, databases, CDC, replication, and streaming/message-based data sources.

Data Pipelines: Builds, optimizes, and operates reliable ETL/ELT pipelines and integrated datasets.

DevOps: Uses CI/CD, automated testing, and deployment practices for data solutions.

Scripting: Uses scripting languages, must have Python, for data engineering automation.

Ownership: Works independently, manages priorities, and drives outcomes with minimal supervision.

Problem Solving: Applies strong analytical, troubleshooting, and root cause analysis skills.