Hadoop Developer

Work with product managers and development leads to create testing strategies · Develop and scale automated data validation framework · Build and monitor key metrics of data health across the entire Big Data pipelines · Early alerting and escalation process to quickly identify and remedy quality issues before something ever goes ‘live’ in front of the customer · Build/refine tools and processes for quick root cause diagnostics · Contribute to the creation of quality assurance standards, policies, and procedures to influence the DQ mind-set across the company
Required skills and experience: · Solid experience working in Big Data ETL environments with Spark and Java/Scala/Python · Strong experience with AWS cloud technologies (EC2, EMR, S3, Kinesis, etc) · Experience building monitoring/alerting frameworks with tools like Newrelic and escalations with slack/email/dashboard integrations, etc · Executive-level communication, prioritization, and team leadership skills

Responsibilities

● Able to contribute to the gathering of functional requirements, developing technical
specifications, and test case planning
● Demonstrating technical expertise, and solving challenging programming and design
problems
● 60% hands-on coding with architecture ownership of one or more products
● Ability to articulate architectural and design options, and educate development teams and
business users
● Resolve defects/bugs during QA testing, pre-production, production, and post-release
patches
● Mentor and guide team members
● Work cross-functionally with various bidgely teams including product management, QA/QE,
various product lines, and/or business units to drive forward results

Requirements
● BS/MS in computer science or equivalent work experience
● 8-12 years’ experience designing and developing applications in Data Engineering
● Hands-on experience with Big data EcoSystems.
● Past experience with Hadoop,Hdfs,Map Reduce,YARN,AWS Cloud, EMR, S3, Spark, Cassandra,
Kafka, Zookeeper
● Expertise with any of the following Object-Oriented Languages (OOD): Java/J2EE,Scala,
Python
● Ability to lead and mentor technical team members
● Expertise with the entire Software Development Life Cycle (SDLC)
● Excellent communication skills: Demonstrated ability to explain complex technical issues to
both technical and non-technical audiences
● Expertise in the Software design/architecture process
● Expertise with unit testing & Test-Driven Development (TDD)
● Business Acumen - strategic thinking & strategy development
● Experience on Cloud or AWS is preferable
● Have a good understanding and ability to develop software, prototypes, or proofs of
concepts (POC's) for various Data Engineering requirements.
● Experience with Agile Development, SCRUM, or Extreme Programming methodologies

Intuitive cloud (http://www.intuitive.cloud">www.intuitive.cloud) is one of the fastest growing top-tier Cloud Solutions and SDx Engineering solution and service company supporting 80+ Global Enterprise Customer across Americas, Europe and Middle East.

Intuitive is a recognized professional and manage service partner for core superpowers in cloud(public/ Hybrid), security, GRC, DevSecOps, SRE, Application modernization/ containers/ K8 -as-a- service and cloud application delivery.

Data Engineering:

• 9+ years’ experience as data engineer.
• Must have 4+ Years in implementing data engineering solutions with Databricks.
• This is hands on role building data pipelines using Databricks. Hands-on technical experience with Apache Spark.
• Must have deep expertise in one of the programming languages for data processes (Python, Scala). Experience with Python, PySpark, Hadoop, Hive and/or Spark to write data pipelines and data processing layers
• Must have worked with relational databases like Snowflake. Good SQL experience for writing complex SQL transformation.
• Performance Tuning of Spark SQL running on S3/Data Lake/Delta Lake/ storage and Strong Knowledge on Databricks and Cluster Configurations.
• Hands on architectural experience
• Nice to have Databricks administration including security and infrastructure features of Databricks.

To introduce myself I head Global Faculty Acquisition for Simplilearn. About My Company: SIMPLILEARN is a company which has transformed 500,000+ carriers across 150+ countries with 400+ courses and yes we are a Registered Professional Education Provider providing PMI-PMP, PRINCE2, ITIL (Foundation, Intermediate & Expert), MSP, COBIT, Six Sigma (GB, BB & Lean Management), Financial Modeling with MS Excel, CSM, PMI - ACP, RMP, CISSP, CTFL, CISA, CFA Level 1, CCNA, CCNP, Big Data Hadoop, CBAP, iOS, TOGAF, Tableau, Digital Marketing, Data scientist with Python, Data Science with SAS & Excel, Big Data Hadoop Developer & Administrator, Apache Spark and Scala, Tableau Desktop 9, Agile Scrum Master, Salesforce Platform Developer, Azure & Google Cloud. : Our Official website : www.simplilearn.com If you're interested in teaching, interacting, sharing real life experiences and passion to transform Careers, please join hands with us. Onboarding Process • Updated CV needs to be sent to my email id , with relevant certificate copy. • Sample ELearning access will be shared with 15days trail post your registration in our website. • My Subject Matter Expert will evaluate you on your areas of expertise over a telephonic conversation - Duration 15 to 20 minutes • Commercial Discussion. • We will register you to our on-going online session to introduce you to our course content and the Simplilearn style of teaching. • A Demo will be conducted to check your training style, Internet connectivity. • Freelancer Master Service Agreement Payment Process : • Once a workshop/ Last day of the training for the batch is completed you have to share your invoice. • An automated Tracking Id will be shared from our automated ticketing system. • Our Faculty group will verify the details provided and share the invoice to our internal finance team to process your payment, if there are any additional information required we will co-ordinate with you. • Payment will be processed in 15 working days as per the policy this 15 days is from the date of invoice received. Please share your updated CV to get this for next step of on-boarding process.

Company Profile
XressBees – a logistics company started in 2015 – is amongst the fastest growing companies of its sector. Our
vision to evolve into a strong full-service logistics organization reflects itself in the various lines of business like B2C
logistics 3PL, B2B Xpress, Hyperlocal and Cross border Logistics.
Our strong domain expertise and constant focus on innovation has helped us rapidly evolve as the most trusted
logistics partner of India. XB has progressively carved our way towards best-in-class technology platforms, an
extensive logistics network reach, and a seamless last mile management system.
While on this aggressive growth path, we seek to become the one-stop-shop for end-to-end logistics solutions. Our
big focus areas for the very near future include strengthening our presence as service providers of choice and
leveraging the power of technology to drive supply chain efficiencies.
Job Overview
XpressBees would enrich and scale its end-to-end logistics solutions at a high pace. This is a great opportunity to join
the team working on forming and delivering the operational strategy behind Artificial Intelligence / Machine Learning
and Data Engineering, leading projects and teams of AI Engineers collaborating with Data Scientists. In your role, you
will build high performance AI/ML solutions using groundbreaking AI/ML and BigData technologies. You will need to
understand business requirements and convert them to a solvable data science problem statement. You will be
involved in end to end AI/ML projects, starting from smaller scale POCs all the way to full scale ML pipelines in
production.
Seasoned AI/ML Engineers would own the implementation and productionzation of cutting-edge AI driven algorithmic
components for search, recommendation and insights to improve the efficiencies of the logistics supply chain and
serve the customer better.
You will apply innovative ML tools and concepts to deliver value to our teams and customers and make an impact to
the organization while solving challenging problems in the areas of AI, ML , Data Analytics and Computer Science.
Opportunities for application:
- Route Optimization
- Address / Geo-Coding Engine
- Anomaly detection, Computer Vision (e.g. loading / unloading)
- Fraud Detection (fake delivery attempts)
- Promise Recommendation Engine etc.
- Customer & Tech support solutions, e.g. chat bots.
- Breach detection / prediction
An Artificial Intelligence Engineer would apply himself/herself in the areas of -
- Deep Learning, NLP, Reinforcement Learning
- Machine Learning - Logistic Regression, Decision Trees, Random Forests, XGBoost, etc..
- Driving Optimization via LPs, MILPs, Stochastic Programs, and MDPs
- Operations Research, Supply Chain Optimization, and Data Analytics/Visualization
- Computer Vision and OCR technologies
The AI Engineering team enables internal teams to add AI capabilities to their Apps and Workflows easily via APIs
without needing to build AI expertise in each team – Decision Support, NLP, Computer Vision, for Public Clouds and
Enterprise in NLU, Vision and Conversational AI.Candidate is adept at working with large data sets to find
opportunities for product and process optimization and using models to test the effectiveness of different courses of
action. They must have knowledge using a variety of data mining/data analysis methods, using a variety of data tools,
building, and implementing models, using/creating algorithms, and creating/running simulations. They must be
comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion
for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.

Roles & Responsibilities
● Develop scalable infrastructure, including microservices and backend, that automates training and
deployment of ML models.
● Building cloud services in Decision Support (Anomaly Detection, Time series forecasting, Fraud detection,
Risk prevention, Predictive analytics), computer vision, natural language processing (NLP) and speech that
work out of the box.
● Brainstorm and Design various POCs using ML/DL/NLP solutions for new or existing enterprise problems.
● Work with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectively
communicating your needs and understanding theirs and address external and internal shareholder's
product challenges.
● Build core of Artificial Intelligence and AI Services such as Decision Support, Vision, Speech, Text, NLP, NLU,
and others.
● Leverage Cloud technology –AWS, GCP, Azure
● Experiment with ML models in Python using machine learning libraries (Pytorch, Tensorflow), Big Data,
Hadoop, HBase, Spark, etc
● Work with stakeholders throughout the organization to identify opportunities for leveraging company data to
drive business solutions.
● Mine and analyze data from company databases to drive optimization and improvement of product
development, marketing techniques and business strategies.
● Assess the effectiveness and accuracy of new data sources and data gathering techniques.
● Develop custom data models and algorithms to apply to data sets.
● Use predictive modeling to increase and optimize customer experiences, supply chain metric and other
business outcomes.
● Develop company A/B testing framework and test model quality.
● Coordinate with different functional teams to implement models and monitor outcomes.
● Develop processes and tools to monitor and analyze model performance and data accuracy.
● Develop scalable infrastructure, including microservices and backend, that automates training and
deployment of ML models.
● Brainstorm and Design various POCs using ML/DL/NLP solutions for new or existing enterprise problems.
● Work with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectively
communicating your needs and understanding theirs and address external and internal shareholder's
product challenges.
● Deliver machine learning and data science projects with data science techniques and associated libraries
such as AI/ ML or equivalent NLP (Natural Language Processing) packages. Such techniques include a good
to phenomenal understanding of statistical models, probabilistic algorithms, classification, clustering, deep
learning or related approaches as it applies to financial applications.
● The role will encourage you to learn a wide array of capabilities, toolsets and architectural patterns for
successful delivery.
What is required of you?
You will get an opportunity to build and operate a suite of massive scale, integrated data/ML platforms in a broadly
distributed, multi-tenant cloud environment.
● B.S., M.S., or Ph.D. in Computer Science, Computer Engineering
● Coding knowledge and experience with several languages: C, C++, Java,JavaScript, etc.
● Experience with building high-performance, resilient, scalable, and well-engineered systems
● Experience in CI/CD and development best practices, instrumentation, logging systems
● Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights
from large data sets.
● Experience working with and creating data architectures.
● Good understanding of various machine learning and natural language processing technologies, such as
classification, information retrieval, clustering, knowledge graph, semi-supervised learning and ranking.

● Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest,
Boosting, Trees, text mining, social network analysis, etc.
● Knowledge on using web services: Redshift, S3, Spark, Digital Ocean, etc.
● Knowledge on creating and using advanced machine learning algorithms and statistics: regression,
simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
● Knowledge on analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Core metrics,
AdWords, Crimson Hexagon, Facebook Insights, etc.
● Knowledge on distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, MySQL, Kafka etc.
● Knowledge on visualizing/presenting data for stakeholders using: Quicksight, Periscope, Business Objects,
D3, ggplot, Tableau etc.
● Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural
networks, etc.) and their real-world advantages/drawbacks.
● Knowledge of advanced statistical techniques and concepts (regression, properties of distributions,
statistical tests, and proper usage, etc.) and experience with applications.
● Experience building data pipelines that prep data for Machine learning and complete feedback loops.
● Knowledge of Machine Learning lifecycle and experience working with data scientists
● Experience with Relational databases and NoSQL databases
● Experience with workflow scheduling / orchestration such as Airflow or Oozie
● Working knowledge of current techniques and approaches in machine learning and statistical or
mathematical models
● Strong Data Engineering & ETL skills to build scalable data pipelines. Exposure to data streaming stack (e.g.
Kafka)
● Relevant experience in fine tuning and optimizing ML (especially Deep Learning) models to bring down
serving latency.
● Exposure to ML model productionzation stack (e.g. MLFlow, Docker)
● Excellent exploratory data analysis skills to slice & dice data at scale using SQL in Redshift/BigQuery.

Expertise in building AWS Data Engineering pipelines with AWS Glue -> Athena -> Quick sight
Experience in developing lambda functions with AWS Lambda
Expertise with Spark/PySpark – Candidate should be hands on with PySpark code and should be able to do transformations with Spark
Should be able to code in Python and Scala.
Snowflake experience will be a plus

• Hands-on experience in any Cloud Platform

· Versed in Spark, Scala/python, SQL

• Microsoft Azure Experience

· Experience working on Real Time Data Processing Pipeline

Technology Skills (Good to Have):

• 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 Executive, 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 building and optimizing ‘big data’ data pipelines, architectures and data sets.
• Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
• Strong analytic skills related to working with unstructured datasets.
• Build processes supporting data transformation, data structures, metadata, dependency and workload management.
• A successful history of manipulating, processing and extracting value from large disconnected datasets.
• Working knowledge of message queuing, stream processing, and highly scalable ‘big data’ data stores.
• Strong project management and organizational skills.
• Experience supporting and working with cross-functional teams in a dynamic environment.
• We are looking for a candidate with 5+ years of experience in a Data Engineer role, who has attained a Graduate degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field. They should also have experience using the following software/tools:

• Experience with big data tools: Hadoop, Spark, Kafka, etc.
• Experience with relational SQL and NoSQL databases, including Postgres and Cassandra.
• Experience with data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc.
• Experience with AWS cloud services: EC2, EMR, RDS, Redshift
• Experience with stream-processing systems: Storm, Spark-Streaming, etc.
• Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.

Skill Set 
SQL, Python, Numpy,Pandas,Knowledge of Hive and Data warehousing concept will be a plus point.

JD 

- Strong analytical skills with the ability to collect, organise, analyse and interpret trends or patterns in complex data sets and provide reports & visualisations.

- Work with management to prioritise business KPIs and information needs Locate and define new process improvement opportunities.

- Technical expertise with data models, database design and development, data mining and segmentation techniques

- Proven success in a collaborative, team-oriented environment

- Working experience with geospatial data will be a plus.