Technical Architect - Data Engineering Uplers

About Uplers

Uplers is on a mission to provide companies all over the globe with the best remote Indian talent to meet their hiring needs. 

We are making an impact in the remote hiring industry by breaking the geographical boundaries and helping companies hire the best of the best without having to worry about sourcing, vetting, retention or motivation of talent. 

In the past 9+ years, Uplers has grown to a family of 1000, serving 7000+ global clients in 52+ countries all across the globe. We measure our success through the people we connect with. We tackle the biggest obstacles that companies face when hiring talent to grow and scale their business by curating a pool of pre-vetted talent that offers quality and priority to our clients while we prioritise and focus on the talent they want to hire. 

We are the matchmakers of the professional world. So, whether you’re a company looking for the right talent or a talent looking for the right company, we got you covered!

Website
https://www.uplers.com/

Job Description

Location - This is an onsite opportunity in Hyderabad 
Shift timing - 11:00 AM to 8:00 PM IST
Salary Range - 10 to 30 % hike on current salary
Years of Experience - 8- 10 Years
Availability to join - Immediate to max 30 days

Note - Need stability at least 2 years of experience with each client. Do not send job hoppers profiles.

Role-  Technical Architect - Data Engineering

• 8+ years of hands-on Experience with crafting and building large scale data pipelines in distributed environments with technologies such as Hadoop, Spark, Kafka, Hive etc.
• Experience with NoSQL datastores like Cassandra, Elasticsearch, HBase, MongoDB.
• Proven skills in designing, tuning & optimizing scalable, highly available distributed systems which can handle high data volumes.
• Strong understanding of software engineering principles and fundamentals including data structures and algorithms.
• Strong experience of working with API’s and integrating multiple applications together. 
• Proficient & hands-on in Python is a must.
• Good Experience with modern engineering practices and technologies including CICD, Cloud native development, social coding (github), chatbots and more.
• Good data modelling experience to address scale and read/write performance.
• Excellent written and oral communication skills on both technical and non-technical topics.
• Excellent general analytical & problem solving skills.
• Experience with cloud computing platforms like AWS or GCP is a plus.

Educational Qualifications:-

Bachelor/Master’s degree in Computer Science, Computer Engineering, quantitative studies, such as Statistics, Math, Operation Research, Economics and Advanced Analytics

Key Responsibilities:-

• Practice Development
• Work with DE Practice Lead and clients to understand business problems, industry context, data sources, potential risks, and constraints.
• Collaborate with Leadership – provide meaningful and credible feedback on Data Engineering capabilities, data availability, and customer trend information.
• Actively mentor and coach the team and help them realize the best solution to a problem 
• Facilitating, guiding, and influencing the clients and teams towards right information technology architecture and becoming interface between Business leadership, Tech leadership and the delivery teams
• Provide best practice advice to customers and team members
• Create an ecosystem that fosters innovation and encourages members of the CoE to build innovative solutions and publish papers/content in public domain.

Project Delivery:-

• Lead our DE team performing standard-to-advanced and the highest complexity Data and API engineering in modern ways on modern platforms and technologies.
• Works with and directs all resources on requirements and reviewing the quality of work prior to move to production.
• Get the stakeholder feedback, get alignment on approaches, deliverables, and roadmaps.
• Develop a project plan including milestones, dates, owners, and risks and contingency plans.
• Create and maintain efficient data pipelines, often within clients’ architecture; typically, data are from a wide variety of sources, internal and external, and manipulated using SQL, spark, and Cloud big data technologies.