Neal Analytics is a cloud, data, and AI Microsoft Gold consulting partner supporting data-driven transformation initiatives from data strategy to solution design, architecture, development, operationalization, activation, and support. Our expertise spans data and application migration and modernization, data science, AI & ML, IoT & edge computing, DevOps & MLOps, Business Intelligence, and Robotic Process Automation. Neal leverages Agile methodologies and flexible engagement models to deliver measurable customer value with a focus on right-sized and pragmatic approaches towards digital transformation. Neal Analytics fosters a culture of technical expertise, cooperation, ownership, and personal growth. Our employees say it best: Neal offers an environment where people are exposed to the most advanced technologies around and where everyone is eager to help, regardless of their team and level in the hierarchy. At Neal, employees can drive customer deliverables end-to-end from day one, and anyone can grow their career through continuous upskilling and a flexible internal job application policy.

 

Job Description:

Neal Analytics is looking for talented Data Engineers! Data Engineers at Neal are professionals who provide the architecture and data transformations needed to serve a Data Science project. They are equipped with first-class skills working with data, but also possess the requisite business acumen to understand high level requirements and build out the details. Our Data Engineers are also responsible for the creation and implementation of the production tools our clients use to interact with the data science outputs. We work in a dynamic, project-based environment that is both interesting and challenging due to the variety of problems. As consultants, our projects span a wide range of applications (marketing, operations, social media, product recommendation, sensor monitoring) and therefore we require a variety of skillsets as much as we require depth of knowledge.

 

Responsibilities:

Team Interaction

• Work with Project Managers, Data Scientists, and Senior Architects to execute tasks/projects
• Understand and interpret the ML process and requirements into the needed data in accordance with project timelines
• Develop the production tools and spec documentation to implement the data science and machine learning outputs into the client’s business processes
• Build the deliverables and front-end consumption interfaces for surfacing insights (Reports, Dashboards, Web & Excel Apps) Client Engagement
• Communicate with clients at all stages of the project cycle to understand requirements and explain limitations
• Senior engineers and architects interact directly with clients, juniors work with Project Managers to surface issues

 

Work Product:

• Expertise in the following tools / technologies:

o Structured and Non-Structured Data processing.

o Data Warehouse and Data Lake Implementation experience in past.

o Data Modelling. o Data Visualization. o Python & T-SQL.

o Experience of working with Migration and Modernization projects.

o Excellent understanding of Azure fundamentals.

o Data Structure and Algorithms.

o Experience on Databricks with Pyspark/Scala.

o Demonstrable excellence in multiple DE coding languages: SQL, .Net, C#, DAX, MDX etc. o Experience with data warehousing principles: Cubes, proper database schemas, etc.

o Knowledge of Big Data & IoT toolsets: EDW, Spark, Hadoop, SQL, Azure IoT suite, etc.

o Deep understanding of at least one analytics platform/stack: Cortana Analytics, AWS, Azure, IBM and a broad understanding of others

• Desire to continuously learn and improve skillset as technology evolves
• Self-manage tasks by prioritizing and executing a plan of attack

   

• Tasks/Duties:

o Data availability: Connecting, moving, and generating datasets for consumption

o Transformation: Feature creation, data cleansing, re-expression

o Visualization: Exploratory data analysis, intuitive presentation of findings

o Dashboarding: Building the deliverable consumption platforms and visuals

o Integration: Linking the data science workflow with client production systems.

 

Education/Qualification Requirements:

• Bachelor’s, Master’s, or PhD in MIS, Computer Science, or a quantitative field or an MBA combined with a technical and relevant bachelor’s degree.
• Proven track record developing data engineering solutions.
• Work experience in a consulting setting is a plus.

Posted On: Thursday, June 30, 2022