About RARA NOW :
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RaRa Now is revolutionizing instant delivery for e-commerce in Indonesia through data-driven logistics.
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RaRa Now is making instant and same-day deliveries scalable and cost-effective by leveraging a differentiated operating model and real-time optimization technology. RaRa makes it possible for anyone, anywhere to get same-day delivery in Indonesia. While others are focusing on - one-to-one- deliveries, the company has developed proprietary, real-time batching tech to do - many-to-many- deliveries within a few hours. RaRa is already in partnership with some of the top eCommerce players in Indonesia like Blibli, Sayurbox, Kopi Kenangan, and many more.
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We are a distributed team with the company headquartered in Singapore, core operations in Indonesia, and a technology team based out of India.
Future of eCommerce Logistics :
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Data driven logistics company that is bringing in same-day delivery revolution in Indonesia
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Revolutionizing delivery as an experience
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Empowering D2C Sellers with logistics as the core technology
About the Role :
- 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.
- Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics.
- Work with stakeholders including the Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
- Keep our data separated and secure across national boundaries through multiple data centers and AWS regions.
- Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.
- Work with data and analytics experts to strive for greater functionality in our data systems.
- Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases
- Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
- Working knowledge of message queuing, stream processing, and highly scalable ‘big data’ data stores.
- Prior experience on working on Big Query, Redshift or other data warehouses
About RaRa Now
RaRa Now revolutionizing Instant and Same-day delivery through tech-innovation for the safest, fastest, and most affordable delivery service.
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Data Engineer JD:
- Designing, developing, constructing, installing, testing and maintaining the complete data management & processing systems.
- Building highly scalable, robust, fault-tolerant, & secure user data platform adhering to data protection laws.
- Taking care of the complete ETL (Extract, Transform & Load) process.
- Ensuring architecture is planned in such a way that it meets all the business requirements.
- Exploring new ways of using existing data, to provide more insights out of it.
- Proposing ways to improve data quality, reliability & efficiency of the whole system.
- Creating data models to reduce system complexity and hence increase efficiency & reduce cost.
- Introducing new data management tools & technologies into the existing system to make it more efficient.
- Setting up monitoring and alarming on data pipeline jobs to detect failures and anomalies
What do we expect from you?
- BS/MS in Computer Science or equivalent experience
- 5 years of recent experience in Big Data Engineering.
- Good experience in working with Hadoop and Big Data technologies like HDFS, Pig, Hive, Zookeeper, Storm, Spark, Airflow and NoSQL systems
- Excellent programming and debugging skills in Java or Python.
- Apache spark, python, hands on experience in deploying ML models
- Has worked on streaming and realtime pipelines
- Experience with Apache Kafka or has worked with any of Spark Streaming, Flume or Storm
Focus Area:
R1 |
Data structure & Algorithms |
R2 |
Problem solving + Coding |
R3 |
Design (LLD) |
2. Responsible for gathering system requirements working together with application architects
and owners
3. Responsible for generating scripts and templates required for the automatic provisioning of
resources
4. Discover standard cloud services offerings, install, and execute processes and standards for
optimal use of cloud service provider offerings
5. Incident Management on IaaS, PaaS, SaaS.
6. Responsible for debugging technical issues inside a complex stack involving virtualization,
containers, microservices, etc.
7. Collaborate with the engineering teams to enable their applications to run
on Cloud infrastructure.
8. Experience with OpenStack, Linux, Amazon Web Services, Microsoft Azure, DevOps, NoSQL
etc will be plus.
9. Design, implement, configure, and maintain various Azure IaaS, PaaS, SaaS services.
10. Deploy and maintain Azure IaaS Virtual Machines and Azure Application and Networking
Services.
11. Optimize Azure billing for cost/performance (VM optimization, reserved instances, etc.)
12. Implement, and fully document IT projects.
13. Identify improvements to IT documentation, network architecture, processes/procedures,
and tickets.
14. Research products and new technologies to increase efficiency of business and operations
15. Keep all tickets and projects updated and track time in a detailed format
16. Should be able to multi-task and work across a range of projects and issues with various
timelines and priorities
Technical:
• Minimum 1 year experience Azure and knowledge on Office365 services preferred.
• Formal education in IT preferred
• Experience with Managed Service business model a major plus
• Bachelor’s degree preferred
• Problem Solving:. Resolving production issues to fix service P1-4 issues. Problems relating to
introducing new technology, and resolving major issues in the platform and/or service.
• Software Development Concepts: Understands and is experienced with the use of a wide range of
programming concepts and is also aware of and has applied a range of algorithms.
• Commercial & Risk Awareness: Able to understand & evaluate both obvious and subtle commercial
risks, especially in relation to a programme.
Experience you would be expected to have
• Cloud: experience with one of the following cloud vendors: AWS, Azure or GCP
• GCP : Experience prefered, but learning essential.
• Big Data: Experience with Big Data methodology and technologies
• Programming : Python or Java worked with Data (ETL)
• DevOps: Understand how to work in a Dev Ops and agile way / Versioning / Automation / Defect
Management – Mandatory
• Agile methodology - knowledge of Jira
Big Data Engineer/Data Engineer
What we are solving
Welcome to today’s business data world where:
• Unification of all customer data into one platform is a challenge
• Extraction is expensive
• Business users do not have the time/skill to write queries
• High dependency on tech team for written queries
These facts may look scary but there are solutions with real-time self-serve analytics:
• Fully automated data integration from any kind of a data source into a universal schema
• Analytics database that streamlines data indexing, query and analysis into a single platform.
• Start generating value from Day 1 through deep dives, root cause analysis and micro segmentation
At Propellor.ai, this is what we do.
• We help our clients reduce effort and increase effectiveness quickly
• By clearly defining the scope of Projects
• Using Dependable, scalable, future proof technology solution like Big Data Solutions and Cloud Platforms
• Engaging with Data Scientists and Data Engineers to provide End to End Solutions leading to industrialisation of Data Science Model Development and Deployment
What we have achieved so far
Since we started in 2016,
• We have worked across 9 countries with 25+ global brands and 75+ projects
• We have 50+ clients, 100+ Data Sources and 20TB+ data processed daily
Work culture at Propellor.ai
We are a small, remote team that believes in
• Working with a few, but only with highest quality team members who want to become the very best in their fields.
• With each member's belief and faith in what we are solving, we collectively see the Big Picture
• No hierarchy leads us to believe in reaching the decision maker without any hesitation so that our actions can have fruitful and aligned outcomes.
• Each one is a CEO of their domain.So, the criteria while making a choice is so our employees and clients can succeed together!
To read more about us click here:
https://bit.ly/3idXzs0
About the role
We are building an exceptional team of Data engineers who are passionate developers and wants to push the boundaries to solve complex business problems using the latest tech stack. As a Big Data Engineer, you will work with various Technology and Business teams to deliver our Data Engineering offerings to our clients across the globe.
Role Description
• The role would involve big data pre-processing & reporting workflows including collecting, parsing, managing, analysing, and visualizing large sets of data to turn information into business insights
• Develop the software and systems needed for end-to-end execution on large projects
• Work across all phases of SDLC, and use Software Engineering principles to build scalable solutions
• Build the knowledge base required to deliver increasingly complex technology projects
• The role would also involve testing various machine learning models on Big Data and deploying learned models for ongoing scoring and prediction.
Education & Experience
• B.Tech. or Equivalent degree in CS/CE/IT/ECE/EEE 3+ years of experience designing technological solutions to complex data problems, developing & testing modular, reusable, efficient and scalable code to implement those solutions.
Must have (hands-on) experience
• Python and SQL expertise
• Distributed computing frameworks (Hadoop Ecosystem & Spark components)
• Must be proficient in any Cloud computing platforms (AWS/Azure/GCP) • Experience in in any cloud platform would be preferred - GCP (Big Query/Bigtable, Pub sub, Data Flow, App engine )/ AWS/ Azure
• Linux environment, SQL and Shell scripting Desirable
• Statistical or machine learning DSL like R
• Distributed and low latency (streaming) application architecture
• Row store distributed DBMSs such as Cassandra, CouchDB, MongoDB, etc
. • Familiarity with API design
Hiring Process:
1. One phone screening round to gauge your interest and knowledge of fundamentals
2. An assignment to test your skills and ability to come up with solutions in a certain time
3. Interview 1 with our Data Engineer lead
4. Final Interview with our Data Engineer Lead and the Business Teams
Preferred Immediate Joiners
Job Title – Data Scientist (Forecasting)
Anicca Data is seeking a Data Scientist (Forecasting) who is motivated to apply his/her/their skill set to solve complex and challenging problems. The focus of the role will center around applying deep learning models to real-world applications. The candidate should have experience in training, testing deep learning architectures. This candidate is expected to work on existing codebases or write an optimized codebase at Anicca Data. The ideal addition to our team is self-motivated, highly organized, and a team player who thrives in a fast-paced environment with the ability to learn quickly and work independently.
Job Location: Remote (for time being) and Bangalore, India (post-COVID crisis)
Required Skills:
- At least 3+ years of experience in a Data Scientist role
- Bachelor's/Master’s degree in Computer Science, Engineering, Statistics, Mathematics, or similar quantitative discipline. D. will add merit to the application process
- Experience with large data sets, big data, and analytics
- Exposure to statistical modeling, forecasting, and machine learning. Deep theoretical and practical knowledge of deep learning, machine learning, statistics, probability, time series forecasting
- Training Machine Learning (ML) algorithms in areas of forecasting and prediction
- Experience in developing and deploying machine learning solutions in a cloud environment (AWS, Azure, Google Cloud) for production systems
- Research and enhance existing in-house, open-source models, integrate innovative techniques, or create new algorithms to solve complex business problems
- Experience in translating business needs into problem statements, prototypes, and minimum viable products
- Experience managing complex projects including scoping, requirements gathering, resource estimations, sprint planning, and management of internal and external communication and resources
- Write C++ and Python code along with TensorFlow, PyTorch to build and enhance the platform that is used for training ML models
Preferred Experience
- Worked on forecasting projects – both classical and ML models
- Experience with training time series forecasting methods like Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA) with Neural Networks (NN) models as Feed-forward NN and Nonlinear Autoregressive
- Strong background in forecasting accuracy drivers
- Experience in Advanced Analytics techniques such as regression, classification, and clustering
- Ability to explain complex topics in simple terms, ability to explain use cases and tell stories
either one of Java, Scala or Python
Experience in Bigdata Technologies (Hadoop/Spark/Hive/Presto/
platforms (Kafka/NiFi/Storm)
Experience in Distributed Search (Solr/Elastic Search), In-memory data-grid
(Redis/Ignite), Cloud native apps and Kubernetes is a plus
Experience in building REST services and API’s following best practices of service
abstractions, Micro-services. Experience in Orchestration frameworks is a plus
Experience in Agile methodology and CICD - tool integration, automation,
configuration management
Added advantage for being a committer in one of the open-source Bigdata
technologies - Spark, Hive, Kafka, Yarn, Hadoop/HDFS
• Total of 4+ years of experience in development, architecting/designing and implementing Software solutions for enterprises.
• Must have strong programming experience in either Python or Java/J2EE.
• Minimum of 4+ year’s experience working with various Cloud platforms preferably Google Cloud Platform.
• Experience in Architecting and Designing solutions leveraging Google Cloud products such as Cloud BigQuery, Cloud DataFlow, Cloud Pub/Sub, Cloud BigTable and Tensorflow will be highly preferred.
• Presentation skills with a high degree of comfort speaking with management and developers
• The ability to work in a fast-paced, work environment
• Excellent communication, listening, and influencing skills
RESPONSIBILITIES:
• Lead teams to implement and deliver software solutions for Enterprises by understanding their requirements.
• Communicate efficiently and document the Architectural/Design decisions to customer stakeholders/subject matter experts.
• Opportunity to learn new products quickly and rapidly comprehend new technical areas – technical/functional and apply detailed and critical thinking to customer solutions.
• Implementing and optimizing cloud solutions for customers.
• Migration of Workloads from on-prem/other public clouds to Google Cloud Platform.
• Provide solutions to team members for complex scenarios.
• Promote good design and programming practices with various teams and subject matter experts.
• Ability to work on any product on the Google cloud platform.
• Must be hands-on and be able to write code as required.
• Ability to lead junior engineers and conduct code reviews
QUALIFICATION:
• Minimum B.Tech/B.E Engineering graduate
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