Data Engineer - Snowflake/AWS/Databricks

INSYSTECH, INC.

Phoenix, AZ, United States

5 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

8 years minimum

Compensation

$85,389.0 - $116,975.0

Working hours

Regular working hours

Job source

www.careerjet.com

Tech stack

Adobe InDesign Airflow Amazon Web Services Amazon S3 Code Review Continuous Integration Information Engineering Data Governance Data Infrastructure Data Integration Extract Transform Load (ETL) Data Warehousing

+12 more

Dimensional Modeling Python (Programming Language) Performance Tuning Query Optimization Sql Optimization Snowflake Apache Spark Git Data Lakes Pyspark Data Pipelines Databricks

Job description

Design, develop, and maintain enterprise data solutions using Snowflake, AWS, and Databricks.
Develop scalable ETL/ELT pipelines for ingestion, transformation, and integration of enterprise data.
Build and optimize Snowflake data models, tables, views, transformations, and queries.
Develop Databricks notebooks and Spark/PySpark processing workflows.
Integrate Snowflake and Databricks with AWS-based data sources and services.
Optimize Snowflake workloads and Databricks processing for performance and cost efficiency.
Implement data-quality, validation, reconciliation, monitoring, and error-handling processes.
Troubleshoot pipeline failures, data discrepancies, and performance issues.
Collaborate with business analysts, architects, application teams, and other Data Engineers.
Participate in design reviews, code reviews, testing, deployment, and production support.
Maintain technical documentation and follow enterprise data governance and security standards.

Requirements

8+ years of overall experience in Data Engineering, ETL/ELT, Data Warehousing, or Data Platform development.
Strong hands-on experience with Snowflake.
Strong hands-on experience with AWS cloud data services.
Strong experience with Databricks.
Advanced SQL skills, including complex transformations, query optimization, and performance tuning.
Experience designing and developing enterprise-scale ETL/ELT pipelines.
Experience integrating data from multiple structured and semi-structured sources.
Strong understanding of data warehousing, dimensional modeling, and data integration.
Experience with Python and/or PySpark for data engineering.
Experience with data-quality validation, monitoring, and production troubleshooting.
Experience working with high-volume enterprise datasets., * Experience with AWS S3, Glue, Lambda, Redshift, Snowpipe, Snowpark, Delta Lake, Airflow, dbt, Git and CI/CD is preferred.