Data Engineer

· Evaluate Cloud Architecture: Provide technical leadership in evaluating, selecting, and implementing the company’s foundational unified cloud data platform.

· Support BI and Analytics: Partner closely with BI and business teams to deliver high-quality, analytics-ready datasets, custom schemas, and self-service data consumption models.

· Design and Build Pipelines: Construct and maintain scalable batch and streaming ETL/ELT pipelines to integrate enterprise applications, product systems, and external sources.

· Ensure Data Quality: Implement automated quality checks, reconciliation processes, and lineage documentation to guarantee accurate business metrics and rapid issue remediation.

· Optimize Workflow Operations: Establish strong pipeline testing, monitoring, alerting, and CI/CD practices to meet data freshness and reporting SLAs.

· Control Costs and Performance: Optimize data storage and compute efficiency through strategic partitioning, indexing, query tuning, and workload management.

· Create Reusable Frameworks: Build and maintain reusable code libraries for data ingestion, transformation, and validation to accelerate delivery of business insights.

· Enforce Security and Governance: Partner with Security and Compliance to implement data access controls, masking, encryption, retention policies, and robust auditability.

·  Document and Share Knowledge: Maintain clear documentation for pipelines, data products, and operational runbooks to ensure team supportability and data definitions.

Required Experience and Education

· Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field, or equivalent work experience.

· Proven experience (5+ years) building and operating production data pipelines and data models.

· Strong proficiency in SQL and at least one programming language commonly used for data engineering (e.g., Python).

· Experience with modern data processing and orchestration concepts (e.g., Spark, dbt, Airflow-like orchestration) and cloud data platforms (e.g., Snowflake, Databricks, MS Fabric, etc.).

· Knowledge of data modeling techniques (dimensional, normalized, and data product approaches) and data quality practices.

· Familiarity with APIs, file-based ingestion, and event/stream processing patterns.

· Strong troubleshooting skills and ability to work across teams to resolve data issues quickly and permanently.

Physical Requirements

· Work is performed in a typical office setting with minimal health or safety hazards exposure—prolonged periods of sitting at a desk and working on a computer.

· Up to 20% travel may be required.

· Moderate lifting/carrying 15-44 lbs; use of fingers, walking/standing 2-6 hours

· Exposure to hazardous materials or various weather conditions