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About the role:
We are seeking an experienced Data Engineer to design, build, and optimize scalable data pipelines and analytics-ready data models using Snowflake, dbt, Astronomer/Airflow, and Python. This role requires strong engineering discipline, hands-on experience with modern data platforms, and the ability to deliver reliable, governed, and high-performing data solutions that enable business reporting, analytics, and decision-making.
What you will be doing
· Design, develop, and maintain scalable batch and/or near-real-time data pipelines using Snowflake, Python, dbt, and Astronomer/Airflow.
· Build robust ELT workflows, reusable data transformation models, and automated data quality checks using dbt.
· Develop and optimize Snowflake data models, warehouses, schemas, tables, views, and stored procedures to support analytics and reporting workloads.
· Orchestrate, schedule, monitor, and troubleshoot data workflows using Astronomer/Airflow, including DAG design, dependency management, retries, and operational alerts.
· Write efficient Python scripts and SQL for data ingestion, transformation, validation, automation, and performance tuning.
· Implement data engineering best practices including modular code, version control, CI/CD readiness, documentation, testing, and peer reviews.
· Collaborate with analysts, product teams, business stakeholders, architects, and platform teams to understand requirements and deliver trusted data products.
· Ensure high standards for data quality, observability, governance, security, lineage, and operational reliability across data pipelines.
What you bring:
· Strong hands-on experience in data engineering, ETL/ELT development, data warehousing, and pipeline orchestration.
· Proficiency in Python and SQL for data ingestion, transformation, automation, validation, and troubleshooting.
· Hands-on experience with Snowflake, including data modeling, query optimization, warehouse management, performance tuning, and secure access patterns.
· Practical experience with dbt for modular transformations, model dependencies, testing, documentation, and deployment workflows.
· Experience with Astronomer/Airflow for DAG development, workflow scheduling, monitoring, retries, alerting, and production support.
· Understanding of data quality, data governance, metadata management, lineage, security controls, and engineering standards for enterprise data platforms.
What we offer you:
· Opportunity to work on high-impact, enterprise-scale data engineering initiatives
· Exposure to modern data platforms, cloud data warehousing, orchestration tools, and analytics engineering practices
· A collaborative environment working closely with product, business, analytics, architecture, and platform teams
· Strong focus on engineering excellence, automation, learning, and career growth
· Ability to build trusted data products that enable reporting, analytics, and business decision-making
Privacy Statement
FIS is committed to protecting the privacy and security of all personal information that we process in order to provide services to our clients. For specific information on how FIS protects personal information online, please see the Online Privacy Notice.
Sourcing Model
Recruitment at FIS works primarily on a direct sourcing model; a relatively small portion of our hiring is through recruitment agencies. FIS does not accept resumes from recruitment agencies which are not on the preferred supplier list and is not responsible for any related fees for resumes submitted to job postings, our employees, or any other part of our company.
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Job details are sourced from the employer's original posting.
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