Analytics Engineer
2 weeks ago
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, GCP, 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
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