Data Engineer – Project Delivery Analyst at Deloitte focuses on building and optimizing data pipelines on AWS and Snowflake, delivering analytics-ready datasets, and enhancing data quality and observability within project delivery teams. The compensation for this role ranges from USD 57,300 to 95,500 per year.
Responsibilities
- Design, build, and enhance data pipelines on AWS using Python to ingest, transform, and deliver data to Snowflake and downstream consumers.
- Develop and maintain Snowflake objects (schemas, tables, views) and implement performant SQL transformations to curate analytics-ready datasets.
- Implement workflow automation and scheduling with tools such as Airflow/MWAA, Step Functions, or Glue, including dependency management, retries, and logging.
- Apply data quality checks and basic observability, covering validation rules, reconciliation, and alerting, and assist with incident triage and remediation.
- Optimize pipeline and query performance with guidance on efficient Python coding, S3 partitioning and file formats, and Snowflake warehouse usage and tuning.
- Follow CI/CD and IaC standards using Git workflows and Terraform or CloudFormation to promote code across environments.
- Collaborate with analysts, product owners, and source-system teams to clarify requirements and validate outputs; participate in sprint ceremonies and estimations.
- Contribute to code reviews, unit tests, and peer debugging; adopt team engineering standards through learning and application.
- Maintain regular communication with Engagement Managers, project team members, and stakeholders from various functional and technical domains, escalating matters requiring engagement-management attention.
- Lead client engagement workstreams focused on process improvement, optimization, and transformation, including implementing leading practice workflows and driving operational outcomes.
Requirements
- At least 1 year of experience building and refining data pipelines and curated analytics-ready datasets for downstream consumers.
- Over 1 year of hands-on SQL and Python experience, including Snowflake and/or PySpark for transformations and scalable processing.
- Minimum 1 year of cloud data engineering on AWS (preferred) or Azure/GCP, with orchestration/scheduling such as Airflow/MWAA, Step Functions, Glue, or ADF/Fabric Data Factory.
- Understanding of ELT patterns and Lakehouse/warehouse concepts; familiarity with S3 formats and partitioning (Parquet, Delta).
- Working knowledge of DevOps practices (Git-based workflows, CI/CD) and exposure to Infrastructure-as-Code (Terraform or CloudFormation).
- Foundational knowledge of data quality, observability, and metadata/governance concepts.
- Bachelor's degree in Computer Science, IT, Computer Engineering, or related field, or equivalent experience.
- Limited immigration sponsorship may be available.
- Ability to travel about 10 percent, depending on client engagement.
Technologies
Python, AWS, Snowflake, PySpark, Airflow, MWAA, Step Functions, Glue, S3, Parquet, Delta, Git, Terraform, CloudFormation, Azure Data Factory, Fabric Data Factory.
Benefits
Discretionary annual incentive program, subject to applicable rules.
The Team
AI and Data - Engineering teams apply advanced capabilities to design, deploy, and operate integrated sector solutions across software, data, AI, network, and hybrid cloud infrastructure. These efforts aim to modernize technology and data platforms while transforming mission-critical operations. The team collaborates with clients to adopt the latest innovations and deliver tailored delivery models that fit engagement needs.
Preferred
- Experience delivering in Agile environments.
- Strong analytical ability to manage multiple projects and translate tasks into concrete work products.
- Ability to work independently or with minimal supervision.
- Excellent written and verbal communication skills.
- Capacity to deliver technical demonstrations.
