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Roles & Responsibilities
- Designing and delivering a best-in-class, highly scalable data governance platform
- Improving processes and applying best practices
- Contribute in all scrum ceremonies; assuming the role of ‘scum master’ on a rotational basis
- Development, management and operation of our infrastructure to ensure it is easy to deploy, scalable, secure and fault-tolerant
- Flexible on working hours as per business needs
● Create and maintain optimal data pipeline architecture.
● Assemble large, complex data sets that meet functional / non-functional
business requirements.
● Building and optimizing ‘big data’ data pipelines, architectures and data sets.
● Maintain, organize & automate data processes for various use cases.
● Identifying trends, doing follow-up analysis, preparing visualizations.
● Creating daily, weekly and monthly reports of product KPIs.
● Create informative, actionable and repeatable reporting that highlights
relevant business trends and opportunities for improvement.
Required Skills And Experience:
● 2-5 years of work experience in data analytics- including analyzing large data sets.
● BTech in Mathematics/Computer Science
● Strong analytical, quantitative and data interpretation skills.
● Hands-on experience with Python, Apache Spark, Hadoop, NoSQL
databases(MongoDB preferred), Linux is a must.
● Experience building and optimizing ‘big data’ data pipelines, architectures and data sets.
● Experience with Google Cloud Data Analytics Products such as BigQuery, Dataflow, Dataproc etc. (or similar cloud-based platforms).
● Experience working within a Linux computing environment, and use of
command-line tools including knowledge of shell/Python scripting for
automating common tasks.
● Previous experience working at startups and/or in fast-paced environments.
● Previous experience as a data engineer or in a similar role.
- Data Engineer
Required skill set: AWS GLUE, AWS LAMBDA, AWS SNS/SQS, AWS ATHENA, SPARK, SNOWFLAKE, PYTHON
Mandatory Requirements
- Experience in AWS Glue
- Experience in Apache Parquet
- Proficient in AWS S3 and data lake
- Knowledge of Snowflake
- Understanding of file-based ingestion best practices.
- Scripting language - Python & pyspark
CORE RESPONSIBILITIES
- Create and manage cloud resources in AWS
- Data ingestion from different data sources which exposes data using different technologies, such as: RDBMS, REST HTTP API, flat files, Streams, and Time series data based on various proprietary systems. Implement data ingestion and processing with the help of Big Data technologies
- Data processing/transformation using various technologies such as Spark and Cloud Services. You will need to understand your part of business logic and implement it using the language supported by the base data platform
- Develop automated data quality check to make sure right data enters the platform and verifying the results of the calculations
- Develop an infrastructure to collect, transform, combine and publish/distribute customer data.
- Define process improvement opportunities to optimize data collection, insights and displays.
- Ensure data and results are accessible, scalable, efficient, accurate, complete and flexible
- Identify and interpret trends and patterns from complex data sets
- Construct a framework utilizing data visualization tools and techniques to present consolidated analytical and actionable results to relevant stakeholders.
- Key participant in regular Scrum ceremonies with the agile teams
- Proficient at developing queries, writing reports and presenting findings
- Mentor junior members and bring best industry practices
QUALIFICATIONS
- 5-7+ years’ experience as data engineer in consumer finance or equivalent industry (consumer loans, collections, servicing, optional product, and insurance sales)
- Strong background in math, statistics, computer science, data science or related discipline
- Advanced knowledge one of language: Java, Scala, Python, C#
- Production experience with: HDFS, YARN, Hive, Spark, Kafka, Oozie / Airflow, Amazon Web Services (AWS), Docker / Kubernetes, Snowflake
- Proficient with
- Data mining/programming tools (e.g. SAS, SQL, R, Python)
- Database technologies (e.g. PostgreSQL, Redshift, Snowflake. and Greenplum)
- Data visualization (e.g. Tableau, Looker, MicroStrategy)
- Comfortable learning about and deploying new technologies and tools.
- Organizational skills and the ability to handle multiple projects and priorities simultaneously and meet established deadlines.
- Good written and oral communication skills and ability to present results to non-technical audiences
- Knowledge of business intelligence and analytical tools, technologies and techniques.
Familiarity and experience in the following is a plus:
- AWS certification
- Spark Streaming
- Kafka Streaming / Kafka Connect
- ELK Stack
- Cassandra / MongoDB
- CI/CD: Jenkins, GitLab, Jira, Confluence other related tools
Who Are We
A research-oriented company with expertise in computer vision and artificial intelligence, at its core, Orbo is a comprehensive platform of AI-based visual enhancement stack. This way, companies can find a suitable product as per their need where deep learning powered technology can automatically improve their Imagery.
ORBO's solutions are helping BFSI, beauty and personal care digital transformation and Ecommerce image retouching industries in multiple ways.
WHY US
- Join top AI company
- Grow with your best companions
- Continuous pursuit of excellence, equality, respect
- Competitive compensation and benefits
You'll be a part of the core team and will be working directly with the founders in building and iterating upon the core products that make cameras intelligent and images more informative.
To learn more about how we work, please check out
Description:
We are looking for a computer vision engineer to lead our team in developing a factory floor analytics SaaS product. This would be a fast-paced role and the person will get an opportunity to develop an industrial grade solution from concept to deployment.
Responsibilities:
- Research and develop computer vision solutions for industries (BFSI, Beauty and personal care, E-commerce, Defence etc.)
- Lead a team of ML engineers in developing an industrial AI product from scratch
- Setup end-end Deep Learning pipeline for data ingestion, preparation, model training, validation and deployment
- Tune the models to achieve high accuracy rates and minimum latency
- Deploying developed computer vision models on edge devices after optimization to meet customer requirements
Requirements:
- Bachelor’s degree
- Understanding about depth and breadth of computer vision and deep learning algorithms.
- 4+ years of industrial experience in computer vision and/or deep learning
- Experience in taking an AI product from scratch to commercial deployment.
- Experience in Image enhancement, object detection, image segmentation, image classification algorithms
- Experience in deployment with OpenVINO, ONNXruntime and TensorRT
- Experience in deploying computer vision solutions on edge devices such as Intel Movidius and Nvidia Jetson
- Experience with any machine/deep learning frameworks like Tensorflow, and PyTorch.
- Proficient understanding of code versioning tools, such as Git
Our perfect candidate is someone that:
- is proactive and an independent problem solver
- is a constant learner. We are a fast growing start-up. We want you to grow with us!
- is a team player and good communicator
What We Offer:
- You will have fun working with a fast-paced team on a product that can impact the business model of E-commerce and BFSI industries. As the team is small, you will easily be able to see a direct impact of what you build on our customers (Trust us - it is extremely fulfilling!)
- You will be in charge of what you build and be an integral part of the product development process
- Technical and financial growth!
Key Responsibilities : ( Data Developer Python, Spark)
Exp : 2 to 9 Yrs
Development of data platforms, integration frameworks, processes, and code.
Develop and deliver APIs in Python or Scala for Business Intelligence applications build using a range of web languages
Develop comprehensive automated tests for features via end-to-end integration tests, performance tests, acceptance tests and unit tests.
Elaborate stories in a collaborative agile environment (SCRUM or Kanban)
Familiarity with cloud platforms like GCP, AWS or Azure.
Experience with large data volumes.
Familiarity with writing rest-based services.
Experience with distributed processing and systems
Experience with Hadoop / Spark toolsets
Experience with relational database management systems (RDBMS)
Experience with Data Flow development
Knowledge of Agile and associated development techniques including:
n
- Data Steward :
Data Steward will collaborate and work closely within the group software engineering and business division. Data Steward has overall accountability for the group's / Divisions overall data and reporting posture by responsibly managing data assets, data lineage, and data access, supporting sound data analysis. This role requires focus on data strategy, execution, and support for projects, programs, application enhancements, and production data fixes. Makes well-thought-out decisions on complex or ambiguous data issues and establishes the data stewardship and information management strategy and direction for the group. Effectively communicates to individuals at various levels of the technical and business communities. This individual will become part of the corporate Data Quality and Data management/entity resolution team supporting various systems across the board.
Primary Responsibilities:
- Responsible for data quality and data accuracy across all group/division delivery initiatives.
- Responsible for data analysis, data profiling, data modeling, and data mapping capabilities.
- Responsible for reviewing and governing data queries and DML.
- Accountable for the assessment, delivery, quality, accuracy, and tracking of any production data fixes.
- Accountable for the performance, quality, and alignment to requirements for all data query design and development.
- Responsible for defining standards and best practices for data analysis, modeling, and queries.
- Responsible for understanding end-to-end data flows and identifying data dependencies in support of delivery, release, and change management.
- Responsible for the development and maintenance of an enterprise data dictionary that is aligned to data assets and the business glossary for the group responsible for the definition and maintenance of the group's data landscape including overlays with the technology landscape, end-to-end data flow/transformations, and data lineage.
- Responsible for rationalizing the group's reporting posture through the definition and maintenance of a reporting strategy and roadmap.
- Partners with the data governance team to ensure data solutions adhere to the organization’s data principles and guidelines.
- Owns group's data assets including reports, data warehouse, etc.
- Understand customer business use cases and be able to translate them to technical specifications and vision on how to implement a solution.
- Accountable for defining the performance tuning needs for all group data assets and managing the implementation of those requirements within the context of group initiatives as well as steady-state production.
- Partners with others in test data management and masking strategies and the creation of a reusable test data repository.
- Responsible for solving data-related issues and communicating resolutions with other solution domains.
- Actively and consistently support all efforts to simplify and enhance the Clinical Trial Predication use cases.
- Apply knowledge in analytic and statistical algorithms to help customers explore methods to improve their business.
- Contribute toward analytical research projects through all stages including concept formulation, determination of appropriate statistical methodology, data manipulation, research evaluation, and final research report.
- Visualize and report data findings creatively in a variety of visual formats that appropriately provide insight to the stakeholders.
- Achieve defined project goals within customer deadlines; proactively communicate status and escalate issues as needed.
Additional Responsibilities:
- Strong understanding of the Software Development Life Cycle (SDLC) with Agile Methodologies
- Knowledge and understanding of industry-standard/best practices requirements gathering methodologies.
- Knowledge and understanding of Information Technology systems and software development.
- Experience with data modeling and test data management tools.
- Experience in the data integration project • Good problem solving & decision-making skills.
- Good communication skills within the team, site, and with the customer
Knowledge, Skills and Abilities
- Technical expertise in data architecture principles and design aspects of various DBMS and reporting concepts.
- Solid understanding of key DBMS platforms like SQL Server, Azure SQL
- Results-oriented, diligent, and works with a sense of urgency. Assertive, responsible for his/her own work (self-directed), have a strong affinity for defining work in deliverables, and be willing to commit to deadlines.
- Experience in MDM tools like MS DQ, SAS DM Studio, Tamr, Profisee, Reltio etc.
- Experience in Report and Dashboard development
- Statistical and Machine Learning models
- Python (sklearn, numpy, pandas, genism)
- Nice to Have:
- 1yr of ETL experience
- Natural Language Processing
- Neural networks and Deep learning
- xperience in keras,tensorflow,spacy, nltk, LightGBM python library
Interaction : Frequently interacts with subordinate supervisors.
Education : Bachelor’s degree, preferably in Computer Science, B.E or other quantitative field related to the area of assignment. Professional certification related to the area of assignment may be required
Experience : 7 years of Pharmaceutical /Biotech/life sciences experience, 5 years of Clinical Trials experience and knowledge, Excellent Documentation, Communication, and Presentation Skills including PowerPoint
- Sr. Data Engineer:
Core Skills – Data Engineering, Big Data, Pyspark, Spark SQL and Python
Candidate with prior Palantir Cloud Foundry OR Clinical Trial Data Model background is preferred
Major accountabilities:
- Responsible for Data Engineering, Foundry Data Pipeline Creation, Foundry Analysis & Reporting, Slate Application development, re-usable code development & management and Integrating Internal or External System with Foundry for data ingestion with high quality.
- Have good understanding on Foundry Platform landscape and it’s capabilities
- Performs data analysis required to troubleshoot data related issues and assist in the resolution of data issues.
- Defines company data assets (data models), Pyspark, spark SQL, jobs to populate data models.
- Designs data integrations and data quality framework.
- Design & Implement integration with Internal, External Systems, F1 AWS platform using Foundry Data Connector or Magritte Agent
- Collaboration with data scientists, data analyst and technology teams to document and leverage their understanding of the Foundry integration with different data sources - Actively participate in agile work practices
- Coordinating with Quality Engineer to ensure the all quality controls, naming convention & best practices have been followed
Desired Candidate Profile :
- Strong data engineering background
- Experience with Clinical Data Model is preferred
- Experience in
- SQL Server ,Postgres, Cassandra, Hadoop, and Spark for distributed data storage and parallel computing
- Java and Groovy for our back-end applications and data integration tools
- Python for data processing and analysis
- Cloud infrastructure based on AWS EC2 and S3
- 7+ years IT experience, 2+ years’ experience in Palantir Foundry Platform, 4+ years’ experience in Big Data platform
- 5+ years of Python and Pyspark development experience
- Strong troubleshooting and problem solving skills
- BTech or master's degree in computer science or a related technical field
- Experience designing, building, and maintaining big data pipelines systems
- Hands-on experience on Palantir Foundry Platform and Foundry custom Apps development
- Able to design and implement data integration between Palantir Foundry and external Apps based on Foundry data connector framework
- Hands-on in programming languages primarily Python, R, Java, Unix shell scripts
- Hand-on experience in AWS / Azure cloud platform and stack
- Strong in API based architecture and concept, able to do quick PoC using API integration and development
- Knowledge of machine learning and AI
- Skill and comfort working in a rapidly changing environment with dynamic objectives and iteration with users.
Demonstrated ability to continuously learn, work independently, and make decisions with minimal supervision
Responsibilities:
- 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.
- Able to adapt and work fast in producing the output which upgrades the decision making of stakeholders using ML.
- To design and develop Machine Learning systems and schemes.
- To perform statistical analysis and fine-tune models using test results.
- To train and retrain ML systems and models as and when necessary.
- To deploy ML models in production and maintain the cost of cloud infrastructure.
- To develop Machine Learning apps according to client and data scientist requirements.
- To analyze the problem-solving capabilities and use-cases of ML algorithms and rank them by how successful they are in meeting the objective.
Technical Knowledge:
- Worked with real time problems, solved them using ML and deep learning models deployed in real time and should have some awesome projects under his belt to showcase.
- Proficiency in Python and experience with working with Jupyter Framework, Google collab and cloud hosted notebooks such as AWS sagemaker, DataBricks etc.
- Proficiency in working with libraries Sklearn, Tensorflow, Open CV2, Pyspark, Pandas, Numpy and related libraries.
- Expert in visualising and manipulating complex datasets.
- Proficiency in working with visualisation libraries such as seaborn, plotly, matplotlib etc.
- Proficiency in Linear Algebra, statistics and probability required for Machine Learning.
- Proficiency in ML Based algorithms for example, Gradient boosting, stacked Machine learning, classification algorithms and deep learning algorithms. Need to have experience in hypertuning various models and comparing the results of algorithm performance.
- Big data Technologies such as Hadoop stack and Spark.
- Basic use of clouds (VM’s example EC2).
- Brownie points for Kubernetes and Task Queues.
- Strong written and verbal communications.
- Experience working in an Agile environment.