Data Engineer
T
Talon Hiring SolutionsData and Analytics
Fully remote with some travel to the home office in Birmingham, ALFull-TimeMiddle
Salary$130,000 – $145,000 / year
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Job Details
- Experience
- 4+ years
- Required Skills
- PythonSQLGitSnowflakeTableauCI/CDData modelingdbt
Requirements
- 4+ years of experience in data engineering, analytics engineering, or a similar role.
- Strong SQL skills with experience developing modular, scalable, and optimized logic in dbt.
- Proficiency in Python for data ingestion, transformation, automation, and web scraping.
- Hands-on experience with Snowflake and modern data engineering practices.
- Experience with data integration and ingestion tools such as Fivetran, Azure Data Factory, APIs, or web scraping.
- Familiarity with Git, version control, branching, code reviews, and CI/CD practices.
- Strong understanding of data modeling, data quality, testing, and documentation.
- Ability to take an ambiguous business need, identify requirements, and develop a practical data solution.
- Experience in retail, CPG, consumer products, or consumer hardware.
- Exposure to streaming or event-driven data, machine learning workflows, and medallion architecture.
- Experience with Tableau, Streamlit, or similar analytics and visualization tools.
Responsibilities
- Build, maintain, and optimize data pipelines that ingest information from multiple source systems into Snowflake.
- Develop and maintain scalable data models using dbt, following bronze, silver, gold, and platinum modeling practices.
- Create reliable data solutions that support reporting, business intelligence, analytics, and downstream applications.
- Use SQL and Python to transform data, automate processes, build ingestion logic, and support integrations such as APIs and web scraping.
- Partner with business teams across Marketing, Operations, Finance, and other departments to understand data needs and translate business questions into practical data solutions.
- Establish and maintain data quality checks, testing, documentation, naming conventions, and data definitions.
- Monitor pipelines, troubleshoot failures, and document root causes and solutions.
- Build semantic models to support natural-language querying.
- Collaborate with the Data & Analytics Lead and Data Scientist to develop datasets that support predictive analytics and machine learning initiatives.
- Participate in project planning, requirements gathering, sprint development, and code reviews.
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