Senior Data Engineer

Flow Control Group•Houston, TX

About The Position

FCG is seeking a Senior Data Engineer to work on our Central Data Platform team, utilizing Snowflake, dbt, and Fivetran, and responsible for end-to-end ELT development from ingestion of raw source system data through our integrated and data consumption layers in Snowflake. This role provides data analysis of raw and transformed data, documents source-to-target mappings, provides Snowflake platform expertise, develops and tests data transformations in dbt, and continues to evolve our dbt and Snowflake environments, including architecture, performance tuning, job monitoring, troubleshooting, and environment management. The Senior Data Engineer works as part of a multi-functional team of data professionals focused on delivering data and evolving our technical infrastructure and processes to help deliver reliable, quality data to our business stakeholders.

Requirements

Minimum 5+ years of experience developing and scaling dbt projects.
Minimum 5+ years of experience with Snowflake.
Proven experience designing and implementing dbt transformations in large-scale enterprise analytics environments.
Deep knowledge of all aspects of the Snowflake environment, from databases to security and AI capabilities.
Experience working with data pipeline tools like Fivetran.
Proven experience designing and managing security requirements.
Experience in supporting data engineering applications.
Minimum of 5 years working with cross-functional teams and managing multiple projects and priorities.
Expert in SQL and data analysis.
Experience with Python and AI.
Strong understanding of data integrations, data warehousing, data modeling, and data engineering.
Expert at creating architecture diagrams and documenting requirements and design details.
Familiarity with Azure and cloud architectures.
Excellent problem-solving skills and attention to detail.
Ability to analyze complex business processes and translate them into technical requirements.
Evaluate performance, traceability, and usability in all designs.
Expertise working to implement data warehouses, data marts, and analytic datasets that align with business objectives.
Ability to analyze data from disparate sources to identify gaps in data or definitions.
Ability to effectively test data products and analyze data by looking for patterns and anomalies in the data that impact deliverables.
Ensure alignment with data standards and data governance.
Strong verbal and written communication skills.
Ability to effectively communicate with technical and non-technical stakeholders.
Ability to work collaboratively in a team environment.
Comfortable working in a fast-paced and dynamic environment.
Proactive and able to work independently with minimal supervision.
Strong commitment to delivering high-quality service to internal and external customers.

Responsibilities

Provide technical expertise in all capabilities of dbt and Snowflake.
Design, build, and manage data transformations in dbt.
Create reusable components and templates and proof-of-concept solutions.
Develop and manage data pipelines as needed.
Implement functionality in Snowflake.
Work with data team members to analyze source data and identify transformation requirements to move data to our data warehouse and data marts.
Design robust, reliable, scalable, and performant systems for large-volume data processing.
Help drive decisions on data architecture and engineering best practices.
Develop and enhance monitoring of workflows and processes.
Collaborate with technical teams to design and implement solutions that meet business requirements and align with best practices.
Design security standards and ensure proper implementation of security.
Ensure our data warehouse processing is optimized for performance.
Collaborate with team members to ensure data loads and transformations flow through the system to deliver data according to requirements.
Provide comprehensive technical support for data warehouse processing.
Develop testing frameworks for solutions to ensure that solutions meet requirements and perform as expected.
Work with business users, technical teams, and data stewards to create documentation and metadata to support understanding of data warehouse processing.
Identify opportunities for process improvements and system enhancements to increase efficiency and effectiveness.