Create and maintain optimal data pipeline architecture ETL/ ELT into Structured data
Assemble large, complex data sets that meet functional / non-functional business requirements and create and maintain multi-dimensional modelling like Star Schema and Snowflake Schema, normalization, de-normalization, joining of datasets.
Expert level experience creating Fact tables, Dimensional tables and ingest datasets into Cloud based tools. Job Scheduling, automation experience is must.
Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
Setup and maintain data ingestion, streaming, scheduling, and job monitoring automation. Connectivity between Data factory, Blob storage, SQL, Power BI needs to be maintained for uninterrupted automation.
Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and “big data” technologies on Azure
Build analytics tools that utilize the data pipeline to provide actionable insight into customer acquisition, operational efficiency, and other key business performance metrics
Work with cross-functional teams including external consultants and IT teams to assist with data-related technical issues and support their data infrastructure needs
Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader
4-8 years of in-depth hands-on experience in data warehousing (Synapse or any OLAP) to support business/data analytics, business intelligence (BI)
Advanced knowledge of SQL and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases and Cloud Data warehouses
Data Model development, additional Dims and Facts creation and creating views and procedures, enable programmability to facilitate Automation
Experience of data compression to improve processing and finetuning SQL programming skills required
Experience building and optimizing “big data” data pipelines, architectures and data sets
Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement
Strong analytic skills related to working with structured and unstructured datasets
Experience with manipulating, processing, and extracting value from large unrelated datasets
Working knowledge of message queuing, stream processing, and highly scalable “big data” stores
Strong analytical and problem-solving skills to be able to structure and solve open ended business problems (pharma experience is highly preferred)