Senior Data Engineer - SQL BI (4- 13 yrs) (SQL, SSIS, SSAS exp)
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About UPS:
Moving our world forward by delivering what matters! UPS is a company with a proud past and an even brighter future. Our values define us. Our culture differentiates us. Our strategy drives us. At UPS we are customer first, people led and innovation driven. UPS’s India based Technology Development Centers will bring UPS one step closer to creating a global technology workforce that will help accelerate our digital journey and help us engineer technology solutions that drastically improve our competitive advantage in the field of Logistics.
‘Future You’ grows as a visible and valued Technology professional with UPS, driving us towards an exciting tomorrow. As a global Technology organization we can put serious resources behind your development. If you are solutions orientated, UPS Technology is the place for you. ‘Future You’ delivers ground-breaking solutions to some of the biggest logistics challenges around the globe. You’ll take technology to unimaginable places and really make a difference for UPS and our customers.
Job Summary:
The Senior Data Engineer - SQL BI supervises and participates in the development of batch and real-time data pipelines utilizing various data analytics processing frameworks in support of Business Intelligence (BI), Data Science, and web application products. This position participates in and supports the integration of data from various data sources, both internal and external. This position performs extract, transform, load (ETL) data conversions, and facilitates data cleansing and enrichment. This position performs full systems life cycle management activities, such as analysis, technical requirements, design, coding, testing, implementation of systems and applications software. This position participates and contributes to synthesizing disparate data sources to create reusable and reproducible data assets. This position contributes to application development through semantic and analytical modeling.
REQUIREMENTS
- 4 plus years of relevant professional experience
- In-depth experience with both on-premises SQL Server (SQL, SSIS, SSAS)
- Some experience in Azure (Databricks, Data Factory, Apache Spark, Python)
- Familiarity with Delta lake, Unity Catalog concepts in Databricks
- Demonstrated awareness of Data Warehouse concepts (Star Schema) and methodologies
- Experience with different types of feed (XML, JSON, etc.)
- Familiarity with Data Visualization tools (Power BI is preferred)
- Experience working within Agile Frameworks
- .NET experience is preferred
- Proficiency in writing Python or Java or C# is preferred.
Additional Information This role will be in-office 3 days a week in Chennai, India
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About Delivery Solutions
Out-of-the-box solutions are provided by Delivery Solutions to retailers, allowing them to provide customer experiences such as curbside delivery, same-day delivery, shipping, in-store pickup, and post-purchase pickup. The company collaborates with some of the most recognizable names in the retail industry, such as Michael's, Sephora, Loblaw, GameStop, Office Depot, Sally Beauty, Total Wine, Belk, and Abercrombie & Fitch.
Its SAAS-based solution is incredibly adjustable and works in combination with e-commerce sites, warehouse management systems, order management systems, and point-of-sale systems to give a highly scalable experience and a base of delighted customers. They have direct connections to the most prominent businesses in same-day delivery, like DoorDash, Uber, Postmates, and Shipt, amongst others, in addition to the most prominent shipping firms, including UPS, FedEx, USPS, and others.
Perks & Benefits @Delivery Solutions:
- Permanent Remote work - (Work from anywhere)
- Broadband reimbursement
- Flexi work hours - (Login/Logout flexibility)
- 21 Paid leaves in a year (Jan to Dec) and 7 COVID leaves
- Two appraisal cycles in a year
- Encashment of unused leaves on Gross
- RNR - Amazon Gift Voucher
- Employee Referral Bonus
- Technical & Soft skills training
- Sodexo meal card
- Surprise on birthday/ service anniversary/new baby/wedding gifts
- Annual trip
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Location:Chennai
Domain knowledge: Data cleaning, modelling, analytics, statistics, machine learning, AI
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· To be part of Digital Manufacturing and Industrie 4.0 projects across Saint Gobain group of companies
· Design and develop AI//ML models to be deployed across SG factories
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- Build analytic systems and predictive models
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- Msc-Statistics/ Phd.Statistics
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· Should have hands-on experience in Databricks, ADF (Azure Data Factory), ADLS, Cosmos DB.
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· Detailed knowledge of cloud-based data warehouses, architecture, infrastructure components, ETL, and reporting analytics tools and environments.
· Skilled with writing, tuning, and troubleshooting SQL queries
· Experience with Big Data technologies such as Data storage, Data mining, Data analytics, and Data visualization.
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- IDEs: Visual Studio, VS Code, MS SQL Server
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B.Tech/M.Tech
Percentage 70% and above
2018 & 2019 passouts
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- A revolutionary way of providing continuous assessments of a child's skill and learning, pointing the way to the child's potential in the future. This as opposed to the traditional one-time, dipstick methodology of a test that hurriedly bundles the child into a slot, that in-turn - declares- the child to be fit for a career in a specific area or a particular set of courses that would perhaps get him somewhere. At the core of our system is a lot of data - both structured and unstructured.
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along with metrics to track their progress
Managing available resources such as hardware, data, and personnel so that deadlines
are met
Analysing the ML algorithms that could be used to solve a given problem and ranking
them by their success probability
Exploring and visualizing data to gain an understanding of it, then identifying
differences in data distribution that could affect performance when deploying the model
in the real world
Verifying data quality, and/or ensuring it via data cleaning
Supervising the data acquisition process if more data is needed
Defining validation strategies
Defining the pre-processing or feature engineering to be done on a given dataset
Defining data augmentation pipelines
Training models and tuning their hyper parameters
Analysing the errors of the model and designing strategies to overcome them
Deploying models to production
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