Data Engineer at Ascentech Services Limited

Role Summary

• You will connect and model complex distributed data sets to build repositories, such as data warehouses, data lakes, using appropriate technologies
• Apply strong expertise in machine-learning, data mining, and information retrieval to design, prototype, and build the next-generation analytics engines and services

Key Responsibilities

• Define data lifecycle, including data models and data sources for analytics platform, gathering data from business and clean the data
• Create and maintain optimal data pipeline architecture
• Conducts advanced statistical analysis to provide actionable insights, identify trends, and measure performance 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
• Performs aggregations on data across various warehousing models
• Work with stakeholders including the Executives, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
• Keep data secure
• Create data tools for analytics and data scientist team members
• Works closely with database teams on topics related to data requirements,cleanliness, accuracy etc.

Requirements

• 5-7 years’ experience in a statistical and/or data science role
• Experience working with large data sets, simulation/ optimization and distributed computing tools
• Practical knowledge across data extraction and transformation tools - traditional ETL tools (Informatica, Altryx)as well as more recent big data tools
• Experience in working with large datasets, relational databases (SQL), and distributed systems (Hadoop, Hive)
• Experience Proficiency in R and/or SAS; additional knowledge of other statistical software (SPSS, RapidMiner, etc.) helpful
• Knowledge in data architecture, defining data retention policies, monitoring performance and advising any necessary infrastructure changes
• Comfortable in dashboard development (Tableau, Powerbi, Qlik, etc) and in developing data analytics models (R, Python, Spark)
• Solid business and collaboration skills, and responsive to service needs and operational demand.