11+ Vertex Jobs in Pune | Vertex Job openings in Pune

• Develop, train, and optimize machine learning models using Python, ML algorithms, deep learning frameworks (e.g., TensorFlow, PyTorch), and other relevant technologies.
• Implement MLOps best practices, including model deployment, monitoring, and versioning.
• Utilize Vertex AI, MLFlow, KubeFlow, TFX, and other relevant MLOps tools and frameworks to streamline the machine learning lifecycle.
• Collaborate with cross-functional teams to design and implement CI/CD pipelines for continuous integration and deployment using tools such as GitHub Actions, TeamCity, and similar platforms.
• Conduct research and stay up-to-date with the latest advancements in machine learning, deep learning, and MLOps technologies.
• Provide guidance and support to data scientists and software engineers on best practices for machine learning development and deployment.
• Assist in developing tooling strategies by evaluating various options, vendors, and product roadmaps to enhance the efficiency and effectiveness of our AI and data science initiatives.

1. 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

Graas uses predictive AI to turbo-charge growth for eCommerce businesses. We are “Growth-as-a-Service”. Graas is a technology solution provider using predictive AI to turbo-charge growth for eCommerce businesses. Graas integrates traditional data silos and applies a machine-learning AI engine, acting as an in-house data scientist to predict trends and give real-time insights and actionable recommendations for brands. The platform can also turn insights into action by seamlessly executing these recommendations across marketplace store fronts, brand.coms, social and conversational commerce, performance marketing, inventory management, warehousing, and last mile logistics - all of which impacts a brand’s bottom line, driving profitable growth.

Roles & Responsibilities:

Work on implementation of real-time and batch data pipelines for disparate data sources.

• Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS technologies.
• Build and maintain an analytics layer that utilizes the underlying data to generate dashboards and provide actionable insights.
• Identify improvement areas in the current data system and implement optimizations.
• Work on specific areas of data governance including metadata management and data quality management.
• Participate in discussions with Product Management and Business stakeholders to understand functional requirements and interact with other cross-functional teams as needed to develop, test, and release features.
• Develop Proof-of-Concepts to validate new technology solutions or advancements.
• Work in an Agile Scrum team and help with planning, scoping and creation of technical solutions for the new product capabilities, through to continuous delivery to production.
• Work on building intelligent systems using various AI/ML algorithms. 

Desired Experience/Skill:

• Must have worked on Analytics Applications involving Data Lakes, Data Warehouses and Reporting Implementations.
• Experience with private and public cloud architectures with pros/cons.
• Ability to write robust code in Python and SQL for data processing. Experience in libraries such as Pandas is a must; knowledge of one of the frameworks such as Django or Flask is a plus.
• Experience in implementing data processing pipelines using AWS services: Kinesis, Lambda, Redshift/Snowflake, RDS.
• Knowledge of Kafka, Redis is preferred
• Experience on design and implementation of real-time and batch pipelines. Knowledge of Airflow is preferred.
• Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)

We are looking for a Data Analyst that oversees organisational data analytics. This will require you to design and help implement the data analytics platform that will keep the organisation running. The team will be the go-to for all data needs for the app and we are looking for a self-starter who is hands on and yet able to abstract problems and anticipate data requirements.
This person should be very strong technical data analyst who can design and implement data systems on his own. Along with him, he also needs to be proficient in business reporting and should have keen interest in provided data needed for business.

Tools familiarity:  SQL, Python, Mix panel, Metabase, Google Analytics,  Clever Tap, App Analytics

Responsibilities

• Processes and frameworks for metrics, analytics, experimentation and user insights, lead the data analytics team
• Metrics alignment across teams to make them actionable and promote accountability
• Data based frameworks for assessing and strengthening Product Market Fit
• Identify viable growth strategies through data and experimentation
• Experimentation for product optimisation and understanding user behaviour
• Structured approach towards deriving user insights, answer questions using data
• This person needs to closely work with Technical and Business teams to get this implemented.

Skills

• 4 to 6 years at a relevant role in data analytics in a Product Oriented company
• Highly organised, technically sound & good at communication
• Ability to handle & build for cross functional data requirements / interactions with teams
• Great with Python, SQL
• Can build, mentor a team
• Knowledge of key business metrics like cohort, engagement cohort, LTV, ROAS, ROE

Eligibility

BTech or MTech in Computer Science/Engineering from a Tier1, Tier2 colleges

Good knowledge on Data Analytics, Data Visualization tools. A formal certification would be added advantage.

We are more interested in what you CAN DO than your location, education, or experience levels.

Send us your code samples / GitHub profile / published articles if applicable.

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 

• 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.