Project - Data Engineer

Responsibilities

• Design and evolve AWS-based data pipelines using Python to ingest, transform, and deliver data to Snowflake and downstream analytics consumers.
• Create and maintain Snowflake objects such as schemas, tables, and views, plus efficient SQL transformations to produce curated, analytics-ready datasets.
• Set up workflow automation and scheduling with tools like Airflow/MWAA, Step Functions, or Glue, ensuring correct dependencies, retries, and comprehensive logging.
• Implement data quality checks and basic observability, including validation rules, reconciliations, and alerting; assist with incident triage and remediation.
• Enhance pipeline and query performance under guidance, leveraging efficient Python, S3 partitioning/file formats (Parquet/Delta), and optimized Snowflake warehouse usage and query tuning.
• Adhere to CI/CD and infrastructure-as-code standards using Git-based workflows and Terraform/CloudFormation changes 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 estimation sessions.
• Contribute to code reviews, develop unit tests, engage in peer debugging, and apply the team's engineering standards.
• Maintain regular communication with Engagement Managers, project team members, and cross-functional stakeholders, escalating matters that require attention from engagement management.
• Lead client engagement workstreams focused on process improvement, optimization, and transformation, including implementing leading practice workflows, addressing quality deficits, and driving operational outcomes.

Requirements

• Minimum of one year of experience building and refining data pipelines and curated datasets for analytics downstream consumers.
• At least one year of hands-on SQL and Python experience, including Snowflake and/or PySpark for transformations and scalable processing.
• Over one 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, including Git-based workflows and CI/CD, and exposure to Infrastructure-as-Code tools such as Terraform or 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 percent, on average, depending on client engagements and sectors served.

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

Compensation

Salary range: USD 57,300 to 95,500 per year.