Senior Data Engineer

The Senior Data Analyst will serve as the bridge between business stakeholders and the Enterprise Data team. This individual will transform data into actionable insights, define business metrics, ensure data quality, and help expand company's AI-ready data platform. They will work closely with Finance, Sales, Operations, Product, Customer Success, and Engineering to deliver trusted analytics, semantic models, dashboards, and data products. Key Responsibilities Business Intelligence & Analytics Develop and maintain executive dashboards, reports, and scorecards using Tableau and Snowflake. Partner with Finance, Sales, Operations, Product, and Customer Success to translate business requirements into analytical solutions. Perform ad hoc analysis to identify trends, business opportunities, and operational improvements. Define, calculate, and maintain standardized KPIs and business metrics across the organization. Present analytical findings and recommendations to business stakeholders and executive leadership. Develop and maintain SQL-based data transformation logic, views, and stored procedures within Snowflake. Support the design, testing, and maintenance of ETL/ELT data pipelines from source systems such as PostgreSQL, SQL Server, Salesforce, APIs, and Amazon S3. Validate data ingestion processes to ensure completeness, consistency, and accuracy across enterprise data platforms. Troubleshoot data pipeline issues, perform root cause analysis, and work with Data Engineers to resolve production incidents. Assist in optimizing SQL queries, data models, and reporting structures for scalability and performance. Participate in deploying data enhancements through Development, QA, Stage, and Production environments while following SDLC and change management processes. Contribute to data modeling initiatives, including dimensional models and curated reporting datasets. Data Quality & Governance Monitor and improve enterprise data quality by identifying anomalies, inconsistencies, and reconciliation issues. Develop data validation processes and reconciliation reports between source systems and Snowflake. Maintain business definitions, data dictionaries, metadata, and documentation for enterprise reporting assets. Collaborate with business and engineering teams to define data standards, governance policies, and reporting consistency. AI & Advanced Analytics Support the development and maintenance of semantic models used by Snowflake AI and enterprise data agents. Validate AI-generated responses and improve metadata, synonyms, and business definitions to enhance accuracy. Assist in evaluating emerging AI capabilities that improve analytics, reporting, and business decision-making. Work closely with Data Engineers, Software Engineers, Product Managers, and business stakeholders to deliver enterprise data solutions. Participate in requirements gathering, solution design, testing, and user acceptance activities. Identify opportunities to automate manual reporting processes and improve operational efficiency. Promote best practices in analytics, data engineering, documentation, and data governance. Preferred Qualifications Bachelor's degree in Computer Science, Information Systems, Data Analytics, Business Analytics, or a related field. 4-7 years of experience in data analytics, business intelligence, or analytics engineering. Advanced SQL with experience writing complex queries, views, stored procedures, and performance tuning. Experience with Snowflake or another cloud data warehouse. Experience with Tableau or a comparable BI platform. Working knowledge of ETL/ELT concepts and modern data architectures. Familiarity with AWS services such as S3, Glue, or Lambda is preferred. Experience working with APIs and semi-structured data (JSON, XML, Parquet). Basic Python scripting for data analysis or automation is a plus. Experience with Git, SDLC processes, and Agile methodologies. Excellent analytical, problem-solving, communication, and stakeholder management skills. This job description reflects someone who can not only build dashboards but also understand how data is ingested, transformed, governed, and consumed- making them an effective bridge between business users and your data engineering team while supporting your Snowflake-based data platform. #J-18808-Ljbffr ASB Resources