Analytics Engineer

Overview

Part of the overall Analytics Group, the Analytics Engineering team is responsible for data modeling automation and preparing data needed by the Data Science team for modeling and the Business Analytics team for data analysis. This will involve documenting the data cleaning and transformation processes, automating these processes, ensuring data governance practices are followed, and maintaining a data catalog. The team is also responsible for developing all Power

BI dashboards based on client needs or Data Science outputs.

• Core Responsibilities:
  
  Data Integration, Data Cleaning, Data Transformation, Data Warehousing, Dashboarding, MLOps, Charts & Visualizations, Reporting Automation, etc.
• Primary Tools:
  Databricks, Azure Synapse, Power BI

As an Analytics Engineer, you play a crucial role in developing and maintaining the data infrastructure that supports our analytics engineering capabilities. You work closely with data scientists, analysts, strategic planners, and other disciplines to ensure data quality and accessibility and implement best practices for data governance and security. Specifically, the Analytics Engineer will:

• Develop and maintain data pipelines and ETL processes.
• Optimize data infrastructure for efficient data processing.
• Ensure data quality and accessibility for data scientists and analysts.
• Collaborate with cross-functional teams to address data needs and challenges.
• Implement data governance and security best practices.
• Support annual planning initiatives with clients.
• Work closely with cross-functional teams, including analysts, product managers and domain experts to understand business requirements, formulate problem statements, and deliver relevant data science solutions.
• Develop and optimize machine learning models by processing, analyzing and extracting data from varying internal and external data sources.
• Develop supervised, unsupervised, and semi-supervised machine learning models using state-of-the-art techniques to solve client problems.
• Show up - be accountable, take responsibility, and get back up when you are down.
• Make stuff.
• Share so others can see what’s happening.

• A bachelor’s/master’s degree in data analytics, computer science, or a directly related field.
• 3-5 years of industry experience in a data analytics or related role.
• Proficiency in SQL for data querying and manipulation.
• Experience with data warehousing solutions.
• Design, implement, and manage ETL workflows to ensure data is accurately and efficiently collected, transformed, and loaded into our data warehouse.
• Proficiency in programming languages such as Python and R.
• Experience with cloud platforms such as AWS, Azure, and Google Cloud.
• Experience in developing and deploying machine learning models.
• Knowledge of machine learning engineering practices, including model versioning, deployment, and monitoring.
• Familiarity with machine learning frameworks and libraries (e.g., Tensor Flow, PyTorch, Scikit-learn).
• Ability to design and develop scalable data pipelines for batch and real-time data processing.
• Experience with big data technologies such as Apache Spark, Hadoop, or similar.
• Proficiency in working with structured and unstructured data sources.
• Knowledge of data governance and security best practices.
• Strong understanding of data modeling techniques and best practices.
• Experience with Dev Ops or MLOps practices for continuous integration and deployment.
• Establish and create scalable and intuitive reporting methodologies through Power BI, suggesting the best representation and visualizations.
• Identify business intelligence needs recommending the best KPIs and customer valuation models and dashboards.
• Interpret data, analyze results, and identify trends or patterns in complex data sets.
• Filter and “clean” data and review computer reports, printouts, and performance indicators to locate and correct data corruption problems.
• Data collection, setting and leveraging DMP and CDP-based infrastructures, attribution modeling, A/B & multivariate testing, and dynamic creative.
• Develop, evaluate, test, and maintain architectures and data solutions such as ETL Pipelines, Data Warehouses, Data…