Project - Data Engineer

Deloitte’s Bellevue, Washington-based project delivery team is looking for a Data Engineer to design and operate data pipelines on AWS and Snowflake, delivering analytics-ready datasets as part of the Project Delivery Model. This on-site role sits at the crossroads of software engineering and data operations, collaborating with analysts, product owners, and source-system teams to translate requirements into reliable data products for client engagements. The position offers a salary range of USD 57,300 to 95,500 per year and is based on-site in Bellevue.

Responsibilities

• Design and extend cloud-based data pipelines on AWS using Python to ingest, transform, and route data to Snowflake and downstream analytics teams.
• Create and manage Snowflake objects such as schemas, tables, and views, implementing efficient SQL transformations to produce curated datasets ready for analytics.
• Automate workflows and scheduling with tools like Airflow/MWAA, Step Functions, and Glue, establishing dependencies, retries, and logging.
• Implement data quality validations, basic observability, and alerting, and assist with incident triage and remediation.
• Enhance pipeline and query performance through efficient Python practices, S3 partitioning and file formats (Parquet/Delta), and tuned Snowflake usage.
• Follow CI/CD and Infrastructure-as-Code standards using Git workflows and Terraform/CloudFormation to promote code across environments.
• Collaborate with analysts, product owners, and source-system teams to clarify requirements, validate outputs, and participate in sprint ceremonies and estimations.
• Contribute to code reviews, unit tests, and peer debugging while adhering to team engineering standards.
• Maintain regular communication with Engagement Managers and project stakeholders across functional and technical teams, escalating issues when needed.
• Lead and participate in client engagement workstreams focused on process improvement, optimization, and transformation, implementing best-practice workflows and addressing quality deficits to drive operational outcomes.

Requirements

• At least 1 year of experience building and enhancing data pipelines and curated datasets for analytics and downstream consumers.
• 1+ year of hands-on experience with SQL and Python, including Snowflake and/or PySpark for transformations and scalable processing.
• 1+ year of cloud data engineering experience on AWS (preferred) or Azure/GCP, with orchestration/scheduling using Airflow/MWAA, Step Functions, Glue, or ADF/Fabric Data Factory.
• Understanding of ELT patterns and lakehouse/warehouse concepts; familiarity with S3 file formats and partitioning (Parquet/Delta).
• Working knowledge of DevOps practices (Git-based workflows, CI/CD) and exposure to Infrastructure-as-Code (Terraform/CloudFormation).
• Knowledge of data quality, basic observability, and metadata/governance fundamentals.
• Bachelor’s degree in Computer Science, Information Technology, Computer Engineering, or a related IT discipline, or equivalent experience.
• Limited immigration sponsorship may be available.
• Ability to travel approximately 10% on average, depending on client engagements.

Technologies

• AWS
• Python
• Snowflake
• SQL
• PySpark
• Airflow
• MWAA
• Step Functions
• Glue
• Terraform
• CloudFormation
• Amazon S3
• Parquet
• Delta Lake
• Azure Data Factory (ADF)
• Fabric Data Factory
• Git