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End-to-End Implementation of BI system

Luxury car maker required a Business Intelligence (BI) system to gain insights about their business processes and gain insights about customer needs and preferences, driving business growth and future opportunities. 

Client: A US-based luxury electric car automaker

Problem: The client had multiple cloud database systems capturing different aspects of the business and the needs and preferences of their customers. They required to bring all the cloud databases into a single data storage solution while also building Business Intelligence (BI) and analytics tools to allow for dashboard and visualization creation. The problem can be thought of has having three parts 

  1. Gather all current data sources into a newly built one data storage system 
  2. Build a foundation for business analytics for various internal departments, so that they can easily get actionable insights and enable data-driven decision making. 
  3. Create enterprise level, interactive BI reports, and dashboards like customer usage reports, customer experience reports or such for the various different departments of the enterprise.

VT Solution: Since the client foresaw a 100x growth with a launch of a new product, one of the requirements was to have a flexible data storage system, that could be scaled rapidly. Therefore, we designed and implemented an AWS data lake to form the basis of data storage system. Bringing in source data from APIs, Legacy RDBMS, SAP, and vehicle files into AWS systems. Building Data Lake, creating data integration and ETL capacities to take raw data from data lake and creating a data warehouse. 

Finally, introducing semantic data models using Power BI to perform analytics, create BI reports and dashboards. Enabled self-service BI for client’s analytics team to utilize the data warehouse and its in-built analytical tools. 

Key Points:

  • Achieved client’s goal by creating an end-to-end implementation of a BI system from the creation of a singular data source, data cleaning, integration, extraction to finally generating BI reports and visualizations
  • Build data lake to form the singular source of raw data throughout the organization, automated data integration and ETL to create a data warehouse. 
  • Built Analytics and BI reporting tools on top of the data storage sources, to allow generation of reports, visualizations, and dashboards for client. 
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Pipeline creation for data streamlining

Client: A US-based manufacturer in the consumer goods and industrial manufacturing section

Problem: The client manufactures thousands of products with a diverse reach in the consumer goods section, while also producing important parts for other industries. With the huge number of products, the client produces high-volume of data. This data stream is extremely large, and it makes it hard for the client to process and analyze the data for real time insights. Even when the data is utilized by the client’s analysts, it needs to be processed and cleaned delaying the time it takes for them to go from data to actionable insights. The client wants a solution that can streamline the analytical process and improve overall efficiency for the analytical process.

VT’s solution: We created reliable pipelines for our client, that would perform the various steps required for data ingestion and data processing. Providing a processed, cleansed, and validated set of data, providing easy to operate on data, simplifying the analytical process. We also implemented a data storage solution that allowed for fast retrieval of the various kinds of metrics and data, enabling faster access to the processed data.

Key points:

  • Achieved client’s business objectives by streamlining the analytical process, introducing automation, and providing easy to retrieve data for their analysts, while reducing time spent on the average analytical process.
  • Provided an end-to-end implementation for the data engineering process, from automation through data pipelines to modern data storage solution to easily store and access data, handling large volume data streams to create easy to access processed data for analytics.
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Segment customer base to offer personalized services

Client: A US-based telecommunications company

Problem: The client has noticed a spike of complaints and negative feedback regarding their unlimited plan, with a variety of customers wanting a plan with low bandwidth. However, the client has also seen a large number of people signing up for unlimited plans recently. The client wants to segment their customer base to allow them to market the appropriate plans to their customers. The problem has two aspects:

  1. Segmenting the customer base based on their needs, so that the client can create plans for each segment of their customer base maximizing profits and customer retention.
  2. Create enterprise level, interactive reports, and dashboards to easily showcase the segmentation and provide a high-level view of the differentiation factors.

VT’s solution: The client has already been collecting data and using it to generate reports, understand customer demands and create plans to fit these customer demands. We decided to add a layer of advanced analytics utilizing Machine Learning (ML) on top of their existing systems. We put together two distinct ML techniques to provide a full view of the segmentation. 

  1. Clustering: We developed advanced clustering models that fit our clients’ organization, creating customer segments, and providing insights on their use pattern, feedback, and demographics. This allowed our client to create personalized plans for their customers, creating different use segments and modulating the plans to fit the exact customer segment. 
  2. Predictive Analytics: We implemented custom analytical tools that defined the growth for each segment, balancing customer retention with maximizing profits by predicting how well the newly formulated plans fit the needs of the clients’ customers. 

Finally, we introduced dashboards and visualization to report and explain the segmentation and the predictive analytics. We connected the advanced analytical system on top of the existing analytics of the client, connecting their data warehouse with our implementation and then with their existing BI system. 

Key Points:

  • Achieved client’s business objectives, by developing advanced analytics models to segment customer base and providing insights regarding customer needs.
  • Implemented advanced analytical models utilizing Machine Learning techniques to provide cutting edge solutions for customer segmentation and predictive analysis. Allowing client to better predict their customer response to the new plans. Balancing customer retention with maximizing profits. 
  • Integrating advanced analytics layer on top of existing analytics, data warehouse and business intelligence systems. 
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Front-End Developer

Responsibilities

  • Interface with internal business partners, program & product managers, understanding requirements and delivering vendor integrations.
  • Add and maintain vendor library integrations for our site properties.
  • Manage and maintain privacy components for our site properties.
  • Adopt and define the standards and best practices in vendor integration/tag management including data integrity, validation, reliability, and documentation.
  • Troubleshoot issues related to 3rd party vendor integrations.
  • Analyze and solve problems at their root, stepping back to understand the broader context.
  • Learn and understand GoDaddy’s revenue tracking platform and know when, how, and which to use and which not to use.
  • Keep up to date with advances in technologies and run pilots to design reviews.
  • Continually improve ongoing processes, automating or simplifying self-service support where possible.
  • Triage many possible courses of action in a high-ambiguity environment, making use of both quantitative analysis and business judgment.

Qualifications

  • Strong JavaScript experience required.
  • Good Node.JS experience required.
  • 7+ years of front-end development experience with a focus on writing performant and optimized code required. Advance knowledge is preferred.
  • Google and Social Ad Platform experience preferred.
  • Experience working with browser inspector and performance tools is preferred.
  • Strong organizational and multitasking skills with ability to balance competing priorities.
  • Strong attention-to-detail and organizational skills.
  • Excellent communication (verbal and written) and interpersonal skills and an ability to effectively communicate with both business and technical teams.

Apply HERE

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Principal Data Engineer

Responsibilities

  • Interface with our Business Analytics & data science teams, gathering requirements and delivering complete BI solutions.
  • Mentor junior engineers.
  • Model data and metadata to support discovery, ad-hoc and pre-built reporting.
  • Design and implement data pipelines using cloud/on-premise technologies
  • Own the design, development, and maintenance of datasets our BA teams will use to drive key business decisions.
  • Develop and promote best practices in data engineering, including scalability, reusability, maintainability, and usability.
  • Tune and ensure compute performance by optimizing queries, databases, files, tables, and processes.
  • Analyze and solve problems at their root, stepping back to understand the broader context.
  • Partner with a data platform team to establish SLAs and resolve any data/process issues.
  • Keep up to date with advances in big data technologies and run pilots to design the data architecture to scale with the increased data volume using AWS.
  • Continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for datasets.
  • Triage many possible courses of action in a high-ambiguity environment, making use of both quantitative analysis and business judgment.

  Qualifications

  • Bachelor’s degree in CS or related technical field.
  • 10+ years of experience in data architecture and business intelligence
  • 3 + years of experience in developing solutions in distributed technologies such as Hadoop, hive and spark
  • Experience in delivering end to end solutions using AWS services – S3, RDS, Kinesis, Glue, Redshift & EMR
  • Expert in data modeling, metadata management, and data quality.
  • SQL performance tuning.
  • Strong organizational and multitasking skills with ability to balance competing priorities.
  • Experience in programming using Python, Java or Scala
  • Excellent communication (verbal and written) and interpersonal skills and an ability to effectively communicate with both business and technical teams.
  • An ability to work in a fast-paced environment where continuous innovation is occurring and ambiguity is the norm.
  • Experience in SQL Server BI stack and with a  BI reporting tool
  • Experience migrating from an on-prem to cloud data platform will be a plus

Apply HERE

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DEVOPS ENGINEER

Responsibilities:

  • Work with data and analytics experts to strive for greater functionality in our data systems and can install Airflow from scratch and configure and maintain administer it.
  • The engineer should be an independent guy and should keep up to date with the Airflow open-source community enhancements. He should work with other team members and communicate on planning to make sure nothing gets impacted because of the installation or configuration of Airflow
  • Create and maintain optimal data pipeline architecture,
  • 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.
  • Work with stakeholders including the Product, Data and Analytic teams to assist with data-related technical issues and support their data infrastructure needs.
  • Implement CI/CD pipelines

Requirements:

  • Experience with building data pipelines using EC2, EMR, RDS, Redshift, Spark, Python
  • Understands data and ability to discuss with analysts and translate to material that data engineers can easily understand
  • Understands and ability to develop data models for traditional relational and distributed technologies
  • Experience with SQL and AWS
  • Experience with engineering process (SDLC, data eng cycle, DataOps, DevOps)
  • Effective in driving meetings, discussions
  • Good project planning, execution skills

Apply HERE

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Technical Program Manager

Responsibilities:

  • Drive efficiency, effectiveness and results!!!
  • Create epics/stories/requirements so that the developers can act on; Maintain the backlog
  • Plan and execute large projects and fit it into incremental delivery model
  • Run scrum ceremonies (grooming, planning, standups and retros)
  • Run requirements, design and code reviews
  • Partner will analytics and other technology teams
  • Be responsible for deliverables
  • Contribute to prioritization, architectural decisions, approach selection and data modelling activities
  • Evangelize the data stack and solutions delivered across analytic partners
  • Manage relationships with stake holders, understand the requirements, prepare roadmap and set expectations
  • Provide operational metrics
  • Provide metrics on team performance (velocity etc.)

Requirements:

  • Experience with Data Solutions using Hive, Spark, S3, EMR, Airflow, Python
  • Understands data and ability to discuss with analysts and translate to material that data engineers can easily understand
  • Understands and ability to develop data models for traditional relational and distributed technologies
  • Experience with Rest APIs and strong SQL
  • Experience with engineering process (SDLC, data eng cycle, DataOps, DevOps)
  • Effective in driving meetings, discussions
  • Good project planning, execution skills

Apply HERE

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