AI Technology Insights Inc
Senior Software Engineer
AI Technology Insights Inc, Snowflake, Arizona, United States, 85937
Compensation:
Depends on Experience (DOE) Responsibilities
Collaborate with Stakeholders – Work closely with business leaders, product managers, and analysts to understand requirements and translate them into technical solutions. Design Scalable Solutions – Architect and develop robust software solutions that address complex business problems and ensure seamless data integration across multiple applications. Data Analysis & Insights – Analyze data from various sources to identify trends, patterns, and actionable insights that drive business decisions. Develop & Optimize Data Pipelines – Design, build, and maintain efficient ETL processes and data pipelines to support analytical and reporting needs. Visualization & Reporting – Create intuitive dashboards and presentations that effectively communicate data-driven insights to both technical and non-technical audiences. Cross-functional Collaboration – Coordinate with engineering, analytics, and business teams to align on data strategies and ensure data accuracy and consistency. Client & Stakeholder Engagement – Facilitate client discussions, present findings, and coordinate feedback loops to refine solutions based on business needs. Technical Leadership – Provide guidance on best practices, mentor junior engineers, and drive innovation in data engineering and analytics. Skills/Experience
BI & Visualization Tools – Proficiency in Tableau, Power BI, or similar tools for data visualization and reporting. Database Expertise – Strong experience with SQL and NoSQL databases for querying, data modeling, and performance optimization. Cloud Data Platforms – Hands-on experience with Azure Data Lake, AWS Redshift, Google BigQuery, or other cloud-based data solutions. ETL & Data Engineering – Expertise in Azure Data Factory (ADF), Apache Spark, Airflow, or other ETL tools to design scalable data pipelines. Programming for Data Processing – Proficiency in Python (Pandas, NumPy, PySpark), R, or similar for data manipulation and analysis. Big Data & Streaming – Familiarity with Kafka, Snowflake, Hadoop, or Databricks for handling large-scale data processing. Problem-Solving & Business Acumen – Ability to translate complex data into actionable insights and align solutions with business goals. Collaboration & Communication – Strong interpersonal skills with a positive, “can-do” attitude to work effectively with cross-functional teams.
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Depends on Experience (DOE) Responsibilities
Collaborate with Stakeholders – Work closely with business leaders, product managers, and analysts to understand requirements and translate them into technical solutions. Design Scalable Solutions – Architect and develop robust software solutions that address complex business problems and ensure seamless data integration across multiple applications. Data Analysis & Insights – Analyze data from various sources to identify trends, patterns, and actionable insights that drive business decisions. Develop & Optimize Data Pipelines – Design, build, and maintain efficient ETL processes and data pipelines to support analytical and reporting needs. Visualization & Reporting – Create intuitive dashboards and presentations that effectively communicate data-driven insights to both technical and non-technical audiences. Cross-functional Collaboration – Coordinate with engineering, analytics, and business teams to align on data strategies and ensure data accuracy and consistency. Client & Stakeholder Engagement – Facilitate client discussions, present findings, and coordinate feedback loops to refine solutions based on business needs. Technical Leadership – Provide guidance on best practices, mentor junior engineers, and drive innovation in data engineering and analytics. Skills/Experience
BI & Visualization Tools – Proficiency in Tableau, Power BI, or similar tools for data visualization and reporting. Database Expertise – Strong experience with SQL and NoSQL databases for querying, data modeling, and performance optimization. Cloud Data Platforms – Hands-on experience with Azure Data Lake, AWS Redshift, Google BigQuery, or other cloud-based data solutions. ETL & Data Engineering – Expertise in Azure Data Factory (ADF), Apache Spark, Airflow, or other ETL tools to design scalable data pipelines. Programming for Data Processing – Proficiency in Python (Pandas, NumPy, PySpark), R, or similar for data manipulation and analysis. Big Data & Streaming – Familiarity with Kafka, Snowflake, Hadoop, or Databricks for handling large-scale data processing. Problem-Solving & Business Acumen – Ability to translate complex data into actionable insights and align solutions with business goals. Collaboration & Communication – Strong interpersonal skills with a positive, “can-do” attitude to work effectively with cross-functional teams.
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