Position: Analytics Engineer

Duration: 2 Years

Location: St. Paul, Minnesota (Onsite from Day 1)

Sample Tasks

• Create datasets for analytical purposes, including datasets for end users to import into desktop tools, and datasets for BI developers to import into analytics and BI tools.
• Build semantic layers and models that enable the rest of the organization to consume analytics more efficiently.
• Build the data pipelines that feed into data science and machine learning models.
• Delivers well-defined, transformed, tested, documented, and code-reviewed datasets for analysis
• Builds data aggregation pipelines using Python, Apache Airflow, Spark, SQL, etc.
• Creates robust data models and architectures to support analytics initiatives
• Identifies and implements optimizations to continually enhance query performance, reduce processing time, and increase overall productivity
• Designs, develops and maintains data pipelines to ensure efficient and reliable ETL processes
• Implements and maintains analytics systems, data warehouses, or data lakes to store and manage structured and unstructured data
• Creates and maintains dashboards, visualizations, and reports using tools such as Tableau, Power BI, or Looker to enable data-driven decision-making
• Ensures data quality and accuracy by implementing data validation, monitoring, and error-handling processes
• Provide knowledge transfer

Qualifications

• 2-5 years of experience in data analytics, data engineering, software engineering, or a similar role
• Expertise in data modeling, ETL development, and data analysis
• Strong ability in SQL for data extraction and manipulation, and proficiency in data warehousing concepts/tools such as Redshift and Snowflake
• Familiarity with cloud-based data platforms such as AWS, Google Cloud Platform, and Azure for data storage and processing
• Substantial programming ability using languages/tools such as R, Python, JavaScript, Scala, and C for data manipulation and scripting
• Solid understanding of relevant data governance, data quality, and data security best practices
• Knowledge of ETL processes, data integration, and data warehousing concepts.
• Familiarity with data visualization tools such as Tableau, Power BI, and Looker
• Knowledge of big data technologies and distributed computing frameworks such as Hadoop and Spark
• Strong problem-solving skills, and the ability to think critically and analytically
• Excellent communication skills to effectively collaborate with cross-functional teams and present insights to business stakeholders
• Ability to collaborate with business stakeholders to understand their analytics needs and deliver comprehensive reports, dashboards, and models