Job description The ideal candidate will be responsible for designing, implementing, and managing Azure cloud environments, DevOps processes, and application infrastructure. A strong emphasis is placed on Infrastructure as Code (IaC) using Terraform. Key Responsibilities Azure Infrastructure Design & Implementation: Build and manage scalable, stable Azure infrastructure using Terraform. CI/CD Pipeline Management: Design, develop, and maintain CI/CD pipelines using Azure DevOps. read more Key Skills containercontinuous integrationkubernetesanalyticalci/cdiacazure devopsdockeransiblecloudscriptinggitdevopspowershelllinuxpaasjenkinsscripting languagesdeploymentcdpythonmicrosoft azureazure cloudazure infrastructureterraformbashiaasawsci cd pipeline

Job description Must Have: 35 years of hands-on experience as a Data Engineer Ability to work as an individual contributor, delivering features/stories within deadlines and quality standards Strong understanding of the Agile development process Excellent programming skills and proficiency in writing complex SQL queries Fast learner with the ability to grasp new tools and technologies in the data engineering ecosystem Good communication skills and ability to interact with clients directly Technical Skills: Programming Languages: Java / Python Big Data Technologies: Spark / PySpark, Hadoop, Hive, YARN, Oozie Cloud Platforms & Services: Cloud data warehouse: Snowflake AWS: EMR, S3, Lambda, RDS/Aurora Testing Frameworks: JUnit, Mockito, PowerMock Databases / Querying: Proficient in SQL across platforms: MySQL, SQL Server, Oracle, Hadoop, Snowflake Version Control: GitHub Project Management Tools: VSTS (Azure DevOps Boards) Build Tools: Maven / Gradle CI/CD Tools: Azure DevOps Pipelines Added Advantage: Prior experience with end-to-end data pipelines in production environments Familiarity with data modeling, ETL design, and real-time data streaming Role: Data Engineer Industry Type: IT Services & Consulting Department: Engineering - Software & QA Employment Type: Full Time, Permanent Role Category: Software Development Education UG: Any Graduate Key Skills Skills highlighted with ‘‘ are preferred keyskills Data EngineeringPython PysparkJavaAWS

Job description Skills Required: Should have a minimum 3+ years in Data Engineering, Data Analytics platform. Should have strong hands-on design and engineering background in AWS, across a wide range of AWS services with the ability to demonstrate working on large engagements. Should be involved in Requirements Gathering and transforming them to into Functionally and technical design. Maintain and optimize the data infrastructure required for accurate extraction, transformation, and loading of data from a wide variety of data sources. Design, build and maintain batch or real-time data pipelines in production. Develop ETL/ELT Data pipeline (extract, transform, load) processes to help extract and manipulate data from multiple sources. Automate data workflows such as data ingestion, aggregation, and ETL processing and should have good experience with different types of data ingestion techniques: File-based, API-based, streaming data sources (OLTP, OLAP, ODS etc) and heterogeneous databases. Prepare raw data in Data Warehouses into a consumable dataset for both technical and nontechnical stakeholders. Strong experience and implementation of Data lakes, Data warehousing, Data Lakehousing architectures. Ensure data accuracy, integrity, privacy, security, and compliance through quality control procedures. Monitor data systems performance and implement optimization strategies. Leverage data controls to maintain data privacy, security, compliance, and quality for allocated areas of ownership. Experience of AWS tools (AWS S3, EC2, Athena, Redshift, Glue, EMR, Lambda, RDS, Kinesis, DynamoDB, QuickSight etc.). Strong experience with Python, SQL, pySpark, Scala, Shell Scripting etc. Strong experience with workflow management & Orchestration tools (Airflow, Should hold decent experience and understanding of data manipulation/wrangling techniques. Demonstrable knowledge of applying Data Engineering best practices (coding practices to DS, unit testing, version control, code review). Big Data Eco-Systems, Cloudera/Hortonworks, AWS EMR etc. Snowflake Data Warehouse/Platform. Streaming technologies and processing engines, Kinesis, Kafka, Pub/Sub and Spark Streaming. Experience of working with CI/CD technologies, Git, Jenkins, Spinnaker, Ansible etc Experience building and deploying solutions to AWS Cloud. Good experience on NoSQL databases like Dynamo DB, Redis, Cassandra, MongoDB, or Neo4j etc. Experience with working on large data sets and distributed computing (e.g., Hive/Hadoop/Spark/Presto/MapReduce). Good to have working knowledge on Data Visualization tools like Tableau, Amazon QuickSight, Power BI, QlikView etc. Experience in Insurance domain preferred. Role: Data Engineer Industry Type: Insurance Department: Engineering - Software & QA Employment Type: Full Time, Permanent Role Category: Software Development Education UG: Any Graduate PG: Any Postgraduate Doctorate: Any Doctorate Key Skills Skills highlighted with ‘‘ are preferred keyskills PysparkAWSPythonSQL Data Engineeringdata engineer

Job description Skills Required: Should have a minimum 6+ years in Data Engineering, Data Analytics platform. Should have strong hands-on design and engineering background in AWS, across a wide range of AWS services with the ability to demonstrate working on large engagements. Should be involved in Requirements Gathering and transforming them to into Functionally and technical design. Maintain and optimize the data infrastructure required for accurate extraction, transformation, and loading of data from a wide variety of data sources. Design, build and maintain batch or real-time data pipelines in production. Develop ETL/ELT Data pipeline (extract, transform, load) processes to help extract and manipulate data from multiple sources. Automate data workflows such as data ingestion, aggregation, and ETL processing and should have good experience with different types of data ingestion techniques: File-based, API-based, streaming data sources (OLTP, OLAP, ODS etc) and heterogeneous databases. Prepare raw data in Data Warehouses into a consumable dataset for both technical and nontechnical stakeholders. Strong experience and implementation of Data lakes, Data warehousing, Data Lakehousing architectures. Ensure data accuracy, integrity, privacy, security, and compliance through quality control procedures. Monitor data systems performance and implement optimization strategies. Leverage data controls to maintain data privacy, security, compliance, and quality for allocated areas of ownership. Experience of AWS tools (AWS S3, EC2, Athena, Redshift, Glue, EMR, Lambda, RDS, Kinesis, DynamoDB, QuickSight etc.). Strong experience with Python, SQL, pySpark, Scala, Shell Scripting etc. Strong experience with workflow management & Orchestration tools (Airflow, Should hold decent experience and understanding of data manipulation/wrangling techniques. Demonstrable knowledge of applying Data Engineering best practices (coding practices to DS, unit testing, version control, code review). Big Data Eco-Systems, Cloudera/Hortonworks, AWS EMR etc. Snowflake Data Warehouse/Platform. Streaming technologies and processing engines, Kinesis, Kafka, Pub/Sub and Spark Streaming. Experience of working with CI/CD technologies, Git, Jenkins, Spinnaker, Ansible etc Experience building and deploying solutions to AWS Cloud. Good experience on NoSQL databases like Dynamo DB, Redis, Cassandra, MongoDB, or Neo4j etc. Experience with working on large data sets and distributed computing (e.g., Hive/Hadoop/Spark/Presto/MapReduce). Good to have working knowledge on Data Visualization tools like Tableau, Amazon QuickSight, Power BI, QlikView etc. Experience in Insurance domain preferred. Role: Data Engineer Industry Type: Insurance Department: Engineering - Software & QA Employment Type: Full Time, Permanent Role Category: Software Development Education UG: Any Graduate PG: Any Postgraduate Doctorate: Any Doctorate Key Skills Skills highlighted with ‘‘ are preferred keyskills Pysparkdata engineerAWSPythonSQL Data Engineering

Job description Job Overview: We seek a highly skilled and motivated Data Engineer with strong expertise in Python, PySpark, AWS, Databricks, and Snowflake to join our dynamic team. The ideal candidate should have hands-on experience with Spark optimization, SQL data processing, and AWS tools and technologies. Exposure to Informatica and data streaming tools is a plus. Key Responsibilities: Design, develop, and optimize scalable data processing pipelines using Spark and PySpark. Implement Spark optimization techniques to enhance performance and efficiency. Work with complex SQL queries for data manipulation and transformation. Collaborate with data scientists, analysts, and engineering teams to meet data needs. Work on AWS Cloud services like Redshift, AWS Glue, SQL Server, and Databricks. Utilize Python for data processing tasks, ensuring modularization and packaging. Basic knowledge of Informatica. Engage in real-time data streaming and integration using tools such as NiFi, Kafka, and EventHub (optional but preferred). Hands-on experience with Snowflake for data warehousing and analytics. Must Have Experience _ Python, Pyspark, SQL, AWS, Spark Required Skills: Spark Expertise: Strong experience in Spark and PySpark, including optimization techniques. Complex SQL: Advanced SQL knowledge and data processing skills. Cloud Exposure: Familiarity with AWS services and Databricks. Python Proficiency: Experience with Python for data processing and scripting. Data Streaming: Experience with NiFi, Kafka, and EventHub is desirable. Snowflake: Hands-on experience with Snowflake for data warehousing and analytics. Qualifications: Bachelors or Masters degree in Computer Science, Information Technology, or related fields. 4-8 years of professional experience in Data Engineering, Data Analytics, or related roles. Role: Data Engineer Industry Type: IT Services & Consulting Department: Engineering - Software & QA Employment Type: Full Time, Permanent Role Category: Software Development Education UG: Any Graduate Key Skills Skills highlighted with ‘‘ are preferred keyskills PysparkSparkAWSPythonSQL SnowflakeKafka

Job description We're hiring a Sr. Software Engineer with 4+ yrs exp. in Python/Java, cloud (AWS/GCP/Azure), NoSQL/RDBMS, CI/CD, and scalable systems. LLMs & RAG exp. is a plus. Strong auth, testing, Git, and problem-solving skills needed. Join our fast-paced team! Health insurance Role: Full Stack Developer Industry Type: Software Product Department: Engineering - Software & QA Employment Type: Full Time, Permanent Role Category: Software Development Education UG: Any Graduate Key Skills Skills highlighted with ‘‘ are preferred keyskills JavaPython NoSQLJavascript

Job description Sr. Python Engineer (5+ years) : Required Skills : Expert in Python, with knowledge of at least one Python web framework Familiarity with some ORM libraries Able to integrate multiple data sources and databases into one system Role: IT Consulting - Other Industry Type: IT Services & Consulting Department: Consulting Employment Type: Full Time, Permanent Role Category: IT Consulting Education UG: Any Graduate Key Skills Python DevelopmentPython

Job description Data engineer JD- 5+ years of experience with cloud data engineering (preferably Azure/ADB/ADF), data pipelines and Spark. Work with Databricks and Write optimized and efficient code using PySpark, SparkSQL and Python. Develop and maintain ETL processes using Databricks notebooks and workflows. Implement and optimize data pipelines for data transformation and integration. Knowledge of one or more SQL variant, preferably PL/SQL and Spark SQL. Write complex SQL queries for data retrieval, manipulation, and analysis. read more Key Skills Skills highlighted with ‘‘ are preferred keyskills PysparkAdf FrameworkAdbPython

Job description Snowflake DB developer + Python + SQL Location : PAN India NP : 0 to 30 days Exp : 5 to 10Years Skill set: Snowflake, Python, SQL and PBI developer. Understand and Implement Data Security, Data Modelling read more Key Skills Skills highlighted with ‘‘ are preferred keyskills PysparkSnowflakeSCALASQLPython

Job description MKS Vision Pvt Ltd About us: MKS Vision is a full spectrum of Information Technology and engineering service provider. We exist to provide increased efficiencies and flexibility that accelerate business performance by adapting the latest cutting-edge technologies for our customers. Our services bring tangible benefits to our customers. MKS Vision will assist you in adopting global services. Website: https://www.mksvision.com/ Job Location: Coimbatore/Hyderabad Below are the roles and associated skills we are looking for: Azure Data Engineer Knowledge of lending data systems and data structures (credit applications, loan origination, collections, payments, dialers, credit bureau data) Proficient in SQL for ETL Experience in the use of tools for ETL Scheduling / Automation and Power BI. Proficient in Data standardization and cleanup, ensure data integrity and diagnostic Strong in Database schema design and normalization Knowledge of cloud-based systems (Azure) for data warehouse building Intermediate knowledge of Python for data cleanup and transformation Knowledge of data documentation and exception/error handling and remediation Ability to build / test / deploy APIs to ingest data from internal and external databases Proficient in JSON, XML and text formats to ingest, parse, transform and load databases Preferred minimum 7+ years of experience, BS degree on computer science, management information systems, statistics, data science, etc., or similar experience. Role: Data warehouse Architect / Consultant Industry Type: FinTech / Payments Department: Engineering - Software & QA Employment Type: Full Time, Permanent Role Category: DBA / Data warehousing Education UG: Any Graduate Key Skills Skills highlighted with ‘‘ are preferred keyskills Power BiSQLData WarehousingETLPython Data CleansingAzure Data FactoryRisk AnalyticsOnpremiseAzure Data LakeData ModelingData AnalyticsData StandardsData Governance