Provide technical leadership to data engineers, setting standards for solution design, coding practices, data governance, and quality.
Define and evolve the enterprise data architecture leveraging Snowflake, Data Vault 2.0, and modern event driven and ELT frameworks.
Architect and oversee the delivery of scalable data pipelines, ingestion frameworks, and transformation processes using Snowflake, Python, Spark, Informatica (PowerCenter/IDMC), AWS Glue, and cloud-native tooling.
Partner with product owners, business stakeholders, architects, and analytics teams to deliver high-impact data solutions.
Ensure operational reliability, data accuracy, and performance of enterprise data warehouse and analytical environments.

Requirements

8+ years of experience in data engineering, with 2+ years in a senior, lead, or architect capacity.
Advanced hands-on expertise in Snowflake, including constant learning on advanced new features.
Strong experience implementing Data Vault 2.0 models and automated ELT frameworks.
Proficiency with ETL/ELT and data integration tools such as Informatica IDMC, PowerCenter, Pentaho, AWS Glue, Control M and Python-based pipelines.
Deep understanding of data warehousing and analytics engineering principles.
Strong SQL expertise with experience in stored procedures, Snowflake Scripting, and complex query optimization.
Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent relevant experience/certifications.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data engineeringdata architecturedata governancedata pipelinesdata transformationSnowflakeData Vault 2.0PythonSparkSQL

Soft Skills

technical leadershipcollaborationcommunicationproblem-solvingstakeholder engagement

Certifications

Bachelor's degree in Computer ScienceBachelor's degree in Information SystemsBachelor's degree in Engineering