Sr. Data Engineer

Position Summary:
We are looking for a Senior Data Engineer on a long-term contract basis.  You will design and build modern data pipelines on dbt, Snowflake, and AWS. You will also work directly with business and IT stakeholders across a large, complex corporate IT environment to turn their needs into clear requirements and working data solutions.
This is a hands-on Sr. Data Engineering role. Most of your time will go to building, optimizing, and running data pipelines and transformation layers. Selected resource will be comfortable working with stakeholders' requirements & tuning a Snowflake query or refactoring a dbt model.
You will support data product owners, data architects, BI developers, data analysts, and data scientists across our business portfolio. You will need to be self-directed, communicate clearly, and be able to work through competing priorities, many systems, and many organizational boundaries.

Key Responsibilities:
Design, build, and maintain scalable ELT/ETL pipelines using dbt for transformation, Snowflake as the cloud data warehouse, and AWS services (S3, Lambda, Glue, EC2, SQS, SNS) for ingestion, orchestration, and integration.
Develop and manage dbt models, tests, documentation, and macros, following analytics engineering best practices (modular design, version control, CI/CD).
Build Python modules (Pandas, NumPy) for data processing, automation, and integration.
Design batch and event-driven data integration architectures.
Troubleshoot, tune, and optimize complex SQL and Snowflake workloads for performance and cost.
Implement data validation, data quality checks, and monitoring so delivered data is accurate and reliable.
Support job orchestration, issue resolution, automation, and continuous improvement of data integration processes.
Use GitHub for version control and collaborative development.
Analyze source systems and data to find gaps, define transformation logic, and confirm business rules.
Explain technical topics, trade-offs, and status clearly to both technical and non-technical audiences, including leadership.

Required Skills & Qualifications:
8+ years in data engineering, ETL/ELT, and data warehouse, data mart, or data lake development.
Strong hands-on dbt experience (required): building models, tests, and documentation in production.
Strong hands-on Snowflake experience (required): data modeling, performance tuning, security, and cost management.
Strong hands-on AWS experience (required): S3, Lambda, Glue, EC2, SQS, SNS, and related services.
Advanced SQL skills, including complex query writing and tuning across relational databases.
Proficiency in Python (Pandas, NumPy) for data engineering and automation.
Nice to have:  Capable Business Analysis experience: requirements gathering, documentation, data mapping, and translating business needs into technical solutions.
Strong stakeholder management and communication skills, 
Experience with GitHub and CI/CD practices for data pipelines.
Experience with Agile/Scrum delivery, including backlog refinement and user story writing.

Preferred Qualifications:
Bachelor’s degree (or foreign equivalent) in Computer Science, Computer Engineering, Information Systems, or a related field.
Experience with cloud integration tools such as Matillion, Informatica Cloud, Talend, or Dell Boomi.
Familiarity with orchestration tools such as Airflow, AWS Step Functions, or dbt Cloud.
Exposure to Spark, streaming platforms, NoSQL, and API-based data integration.
Experience with data quality, metadata management, or data catalog tools.