Analyst - Data Engineer

Mission:

• Build high quality data pipelines driving analytic solutions. These solutions will generate insights from our connected data, enabling the advancement of the data-driven decision-making capabilities of our enterprise.
  
• Connect and model complex distributed data sets to build repositories, such as data warehouses, data lakes, using appropriate technologies.
  
• Manage data related contexts ranging across addressing small to large data sets, structured/unstructured or streaming data, extraction, transformation, curation, modelling, building data pipelines, identifying right tools, writing SQL/Java/Scala code, etc.
  

Education:

• First degree in Mathematics, Statistics, MIS, Computer Science, Engineering or other related disciplines.
  
• Fluent in English
  

• Deep knowledge in data architecture, defining data retention policies, monitoring performance and advising any necessary infrastructure changes
  
• Expertise in SQL & data analysis, experience with at least one programming language
  
• Experience developing and maintaining data warehouses in big data solutions
  
• Experience with developing solutions on cloud computing services and infrastructure in the data and analytics space (preferred)
  
• Database development experience using Hadoop or BigQuery and experience with a variety of relational, NoSQL, and cloud database technologies
  
• Worked with BI tools such as Tableau, Power BI, Looker, Shiny
  
• Conceptual knowledge of data and analytics, such as dimensional modeling, ETL, reporting tools, data governance, data warehousing, structured and unstructured data.
  
• Comfortable in dashboard development (Tableau, Powerbi, Qlik, etc) and in developing data analytics models (R, Python, Spark)
  
• Big Data Development experience using Hive, Impala, Spark and familiarity with Kafka (Preferred)
  
• Exposure to machine learning, data science, computer vision, artificial intelligence, statistics, and/or applied mathematics
  

• Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL principles and business goals
  
• Assemble large, complex data sets that meet functional / non-functional business requirements
  
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
  
• Build the infrastructure required for optimal extraction transformation, and loading of data from a wide variety of data sources using SQL and AWS ‘big data’ technologies
  
• Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics
  
• Keep data secure
  
• Lead the evaluation, implementation and deployment of emerging tools and process for analytic data engineering in order to improve our productivity as a team
  
• Work with data and analytics experts to strive for greater functionality data systems
  
• Develop and deliver communication and education plans on analytic data engineering capabilities, standards, and processes
  
• Partner with business analysts and solutions architects to develop technical architectures for strategic enterprise projects and initiatives.