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azure data engineer

data engineer

Quantum Integrators

<div>
 
 
 Role: Data Engineer
 
 
 
 
 Location: Pune/Nagpur
 
 
 
 
 Notice Period: Up to 30 days
 
 
 
 
 Shift Timing: UK Shift
 
 
 
 
 Job Description:
 
 
 
 As part of our expanding GenAI &amp; Data Engineering initiatives, we&rsquo;re looking for a Data Engineer to join our team in Pune and work on enterprise-scale GenAI projects, including clients in regulated industries (Pharma, Life Sciences, Banking).
 
 
 
 
 
 
 What you&rsquo;ll work on:
 
 
 <ul>
 <li>
 Design, build, and maintain scalable data pipelines supporting AI and GenAI use cases
 </li>
 <li>
 Work on modern data platforms (data lakes, lakehouse, streaming, Event hub like Kafka, containers, K8s)
 </li>
 <li>
 Enable high-quality, compliant data for analytics, ML, and GenAI workloads
 </li>
 <li>
 Collaborate closely with AI/ML and GenAI teams (RAG, embeddings, agents)
 </li>
 <li>
 Ensure data security, governance, and regulatory compliance in delivery
 </li>
 </ul>
 
 
 
 
 
 What we&rsquo;re looking for:
 
 
 <ul>
 <li>
 3&ndash;5 years of experience in Data Engineering
 </li>
 <li>
 Strong hands-on skills in SQL and Python
 </li>
 <li>
 DevOps experience will be a huge plus
 </li>
 <li>
 Experience with cloud-based data platforms (AWS / Azure / GCP), Any cloud Architect certification will be preferred (even at Associate level but not practitioner)
 </li>
 <li>
 Understanding of data modeling, ETL/ELT, orchestration, and performance tuning
 </li>
 <li>
 Exposure to regulated environments (Pharma GxP, Life Sciences, BFSI, Healthcare) is a strong plus
 </li>
 <li>
 Good communication skills and ability to work in a global delivery model.
 </li>
 </ul>
 
 
</div>

Data Engineer

Infosys

<div><h2 class="jobSectionHeader">Job Description:</h2> 
<ul>
<li>We are seeking an experienced Data Engineer with strong expertise in Azure Data Services Big Data technologies and ETL ELT processes</li>
<li>The ideal candidate will have domain knowledge in CPG and Manufacturing sectors along with hands on experience in data governance data modeling and cloud based data engineering</li>
<li>This role involves building scalable data pipelines integrating industrial data platforms and enabling advanced analytics for smarter decision making</li></ul> 
<h2 class="jobSectionHeader">
Key Responsibilities:</h2> 
<ul>
<li>Design and develop data pipelines using Azure Data Factory Azure Databricks and Azure Data Lake Gen2</li>
<li>Implement ETL ELT processes data integration techniques and data governance standards</li>
<li>Work with Big Data technologies such as Apache Spark and PySpark for large scale data processing</li>
<li>Gather engineer and prepare data for analytics and business insights</li>
<li>Integrate systems and data across diverse environments ensuring seamless connectivity</li>
<li>Build solutions to extract and consolidate data from multiple sources sales inventory product customer databases</li>
<li>Collaborate with ML Engineers Data Scientists and business stakeholders to enable automation and advanced analytics</li>
<li>Contribute to coding standards and best practices for data engineering workloads</li>
<li>Support modernization initiatives aligned with Industry 4</li>
<li>0 and ISA 95 standards</li></ul> 
<h2 class="jobSectionHeader">
Technical Requirements:</h2> 
<ul>
<li>5 7 years of experience in data engineering and data governance within industrial domains CPG Manufacturing</li>
<li>Strong knowledge of Azure Data Services Azure Data Factory Azure Databricks Azure Data Lake Gen2</li>
<li>Expertise in ETL processes data integration tools and pipeline development</li>
<li>Proficiency in Python SQL and Apache Spark</li>
<li>Understanding of Industry 4</li>
<li>0 ISA 95 standards and maintenance strategies</li>
<li>Familiarity with AI ML concepts and ability to work with advanced analytics teams</li>
<li>Experience with DevOps CI CD for data pipeline automation</li></ul> 
<h2 class="jobSectionHeader">
Additional Responsibilities:</h2> 
<ul>
<li>Good to have</li>
<li>Exposure to data modeling data warehousing and cloud architecture</li>
<li>Knowledge of business intelligence tools and statistical programming</li>
<li>Ability to research and implement new tools for automation and optimization</li></ul> 
<h2 class="jobSectionHeader">
Preferred Skills:</h2>
Technology-&gt;Data Engineering-&gt;Databricks,Technology-&gt;Cloud Integration-&gt;Azure Data Factory (ADF),Technology-&gt;Cloud Platform-&gt;Azure Analytics Services-&gt;Azure Data Lake,Technology-&gt;Consulting - Data Governance-&gt;Data Governance,Technology-&gt;Database-&gt;Data Modeling,Technology-&gt;OpenSystem-&gt;Python - OpenSystem</div>

Yugn

About the role:We are seeking a talented Data Engineer to join our growing data team. The ideal candidate will have strong expertise in Google Cloud Platform (GCP), BigQuery, Python, and SQL, and will be responsible for designing and maintaining scalable data solutions that power business insights.Responsibility:Design, develop, and maintain robust data pipelines and ETL workflows.Manage large-scale datasets using GCP and BigQuery for optimal performance.Write clean, efficient Python and SQL code for data processing and transformation.Collaborate with data analysts, scientists, and business teams to ensure data quality and availability.Optimize workflows for scalability, cost-efficiency, and performance.Skills/Qualifications:3&ndash;4 years of professional experience as a Data Engineer.Hands-on expertise in GCP, BigQuery, Python, and SQL.Strong understanding of data warehousing concepts, ETL processes, and cloud architecture.Proficiency in data modeling, performance tuning, and query optimization.Familiarity with CI/CD pipelines, version control (Git), and workflow orchestration tools (e.g., Airflow).Excellent problem-solving skills, attention to detail, and ability to work in cross-functional teams.Strong communication and collaboration skills.Nice-to-Have:Experience with Apache Spark for distributed data processing.Knowledge of Kubernetes for container orchestration and deployment.Exposure to Machine Learning pipelines and integration with data workfloJob Types: Full-time, PermanentPay: ₹1,200,000.00 - ₹2,500,000.00 per yearBenefits:<ul><li>Health insurance</li><li>Provident Fund</li></ul>Work Location: In person

Fusemachines

<div>About Fusemachines
Fusemachines is a leading AI strategy, talent, and education services provider. Founded by Sameer Maskey Ph.D., Adjunct Associate Professor at Columbia University, Fusemachines has a core mission of democratizing AI. With a presence in 4 countries (Nepal, United States, Canada, and Dominican Republic and more than 450 full-time employees). Fusemachines seeks to bring its global expertise in AI to transform companies around the world. 
 
Type: Full-time, Remote
About the role
This is a remote full-time position responsible for designing, building, testing, optimizing and maintaining the infrastructure and code required for data integration, storage, processing, pipelines and analytics (BI, visualization and Advanced Analytics) from ingestion to consumption, implementing data flow controls, and ensuring high data quality and accessibility for analytics and business intelligence purposes. This role requires a strong foundation in programming, and a keen understanding of how to integrate and manage data effectively across various storage systems and technologies.
We are looking for a skilled Data Engineer with a strong background in Python, SQL, Pyspark and AWS cloud-based large scale data solutions with a passion for data quality, performance and cost optimization. The ideal candidate will develop in an Agile environment.
This role is perfect for an individual passionate about leveraging data to drive insights, improve decision-making, and support the strategic goals of the organization through innovative data engineering solutions.
Qualification &amp; Experience<ul><li>
Must have a full-time Bachelor's degree in Computer Science Information Systems, Engineering, or a related field.</li><li>
At least 2 years of experience as a data engineer with strong expertise in Python, SQL, PySpark and AWS in an Agile environment, with a proven track record of building and optimizing data pipelines, architectures, and datasets, and proven experience in data storage, modeling, management, lake, warehousing, processing/transformation, integration, cleansing, validation and analytics.</li><li>
2+ years of experience with DevOps tools and technologies: GitHub or AWS DevOps.</li><li>
Proven experience delivering large scale projects and products for Data and Analytics, as a data engineer within AWS.</li><li>
Preferred previous experience working with retail or other similar data models.</li><li>
Following certifications:<ul><li>
AWS Certified Cloud Practitioner</li><li>
AWS Certified Data Engineer - Associate</li><li>
Nice to have:<ul><li>
Databricks Certified Associate Developer for Apache Spark</li><li>
Databricks Certified Data Engineer Associate</li></ul></li></ul></li></ul>
Required skills/Competencies<ul><li>
Strong programming Skills in one or more object-oriented languages such as Python (must have), Scala, Java, and proficiency in writing high-quality, scalable, maintainable, efficient and optimized code for data integration, storage, processing, manipulation and analytics solutions.</li><li>
Strong SQL skills and experience working with complex data sets, Enterprise Data Warehouse and writing advanced SQL queries. Proficient with Relational Databases (RDS, MySQL, Postgres, or similar) and NonSQL Databases (Cassandra, MongoDB, Neo4j, etc.).</li><li>
Strong analytic skills related to working with structured and unstructured datasets.</li><li>
Thorough understanding of big data principles, techniques, and best practices.</li><li>
Experience with scalable and distributed Data Processing Technologies such as Spark/PySpark (must have including Spark SQL) and Kafka, to be able to handle large volumes of data.</li><li>
Experience with stream-processing systems: Storm, Spark-Streaming, etc. is a plus.</li><li>
Experience in implementing data pipelines and efficient ELT/ETL processes, batch and real-time, in AWS and using open source solutions, being able to develop custom integration solutions as needed, including Data Integration from different sources such as APIs (PoS integrations is a plus), ERP (Oracle and Allegra are a plus), databases, flat files, Apache Parquet, event streaming, including cleansing, transformation and validation of the data.</li><li>
Experience in data cleansing, transformation, and validation.</li><li>
Understanding of Data Modeling and Database Design Principles. Being able to implement efficient database schemas that meet the requirements to support data solutions. With good understanding of dimensional data modeling</li><li>
Knowledge in cloud computing specifically in AWS services related to data and analytics, such as S3, EMR, Glue, SageMaker, RDS, Redshift, Lambda, Kinesis, Lake Formation, EC2, ECS/ECR, EKS, IAM, CloudWatch, etc. implementing Data Warehousing, data lake and data lake house, solutions in AWS.</li><li>
Experience in Orchestration using technologies like Azkaban, Luigi, Airflow, etc..</li><li>
Good understanding of BI solutions including Looker and LookML (Looker Modeling Language)</li><li>
Familiar with advanced analytics, AI/ML services and tools, and the ability to integrate advanced analytics, machine learning, and AI capabilities into data solutions, nice to have.</li><li>
Strong understanding of the software development lifecycle (SDLC), especially Agile methodologies.</li><li>
Knowledge of SDLC tools and technologies, including project management software (Jira or similar), source code management (GitHub, AWS CodeCommit or similar), CI/CD system (GitHub actions, Jenkins, AWS CodePipeline or similar) and binary repository manager (Sonatype Nexus, AWS CodeArtifact or similar).</li><li>
Knowledge and hands-on experience of DevOps principles, tools and technologies (GitHub and AWS DevOps) including continuous integration, continuous delivery (CI/CD), infrastructure as code (IaC &ndash; Terraform), configuration management, automated testing, performance tuning and cost management and optimization.</li><li>
Knowledge of data structures and algorithms and good software engineering practices.</li><li>
Strong analytical skills to identify and address technical issues, performance bottlenecks, and system failures.</li><li>
Proficiency in debugging and troubleshooting issues in complex data and analytics environments and pipelines.</li><li>
Understanding of Data Quality and Governance, including implementation of data quality and integrity checks and monitoring processes to ensure that data is accurate, complete, and consistent.</li><li>
Good Problem-Solving skills: being able to troubleshoot data processing pipelines and identify performance bottlenecks and other issues.</li><li>
Strong interpersonal skills and ability to work with a wide range of stakeholders.</li><li>
Excellent communication skills to collaborate with cross-functional teams, including business users, data architects, DevOps/DataOps/MLOps engineers, data analyst, data scientists, developers, and operations teams. Essential to convey complex technical concepts and insights to non-technical stakeholders effectively.</li><li>
Ability to document processes, procedures, and deployment configurations.</li><li>
Understanding of security practices, including network security groups, encryption, and compliance standards, and ability to implement security controls and best practices within data and analytics solutions, including proficient knowledge and working experience on various cloud security vulnerabilities and ways to mitigate them.</li><li>
Self-motivated with the ability to work well in a team.</li><li>
Strong project management and organizational skills.</li><li>
A willingness to stay updated with the latest services, Data Engineering trends, and best practices in the field.</li><li>
Comfortable with picking up new technologies independently and working in a rapidly changing environment with ambiguous requirements.</li><li>
Care about architecture, observability, testing, and building reliable infrastructure and data pipelines.</li></ul>
Responsibilities:<ul><li>
Design, implement, deploy, test and maintain highly scalable and efficient data architectures, defining and maintaining standards and best practices for data management independently with minimal guidance.</li><li>
Ensure systems meet business requirements and industry practices for data integrity, performance, and reliability.</li><li>
Integrate new data management technologies and software engineering tools into existing structures.</li><li>
Create custom software components and analytics applications.</li><li>
Employ a variety of languages and tools to marry systems together or try to hunt down opportunities to improve current processes.</li><li>
Evaluate and advise on technical aspects of open work requests in the data pipeline with the project team.</li><li>
Handle ELT/ETL processes, including data extraction, loading and transformation, from different sources ensuring consistency and quality.</li><li>
Transform and clean data for further analysis and storage.</li><li>
Design and optimize data models and schemas to support business requirements and analysis.</li><li>
Implement monitoring tools and systems to ensure the availability and performance of data systems.</li><li>
Manage data security and access, ensuring confidentiality and integrity.</li><li>
Automate repetitive tasks and processes to improve operational efficiency.</li><li>
Collaborate with data science teams to establish pipelines and workflows for training, validation, deployment, and monitoring of machine learning models. Automate deployment and management of machine learning models in production environments.</li><li>
Contribute to data quality assurance efforts, such as implementing data validation checks and tests to ensure reliability, efficiency, accuracy, completeness and consistency of data.</li><li>
Test software solutions and meet product quality standards prior to release to QA.</li><li>
Ensure the reliability, scalability, and efficiency of data systems are maintained at all times. Identifying and resolving performance bottlenecks in pipelines due to data, queries and processing workflows to ensure efficient and timely data delivery.</li><li>
Work with DevOps teams to optimize resources.</li><li>
Assist in the configuration and management of data warehousing and data lake solutions.</li><li>
Collaborate closely with cross-functional teams including Product, Engineering, Data Scientists, and Analysts to thoroughly understand data requirements and provide data engineering support and extend the company&rsquo;s data with third-party sources of information when needed.</li><li>
Takes ownership of storage layer, database management tasks, including schema design, indexing, and performance tuning.</li><li>
Evaluate and implement cutting-edge technologies and methodologies and continue learning and expanding skills in data engineering and cloud platforms, to improve and modernize existing data systems.</li><li>
Develop, design, and execute data governance strategies encompassing cataloging, lineage tracking, quality control, and data governance frameworks that align with current analytics demands and industry best practices working closely with Data Architect.</li><li>
Ensure technology solutions support the needs of the customer and/or organization.</li><li>
Define and document data engineering architectures, processes and data flows.</li></ul>
Equal Opportunity Employer: Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.
XudWe5nO8A</div>

Amgen

<div>
 
 Role Name:
 
 Data Engineer
 
 
 
 Department Name:
 
 XXX
 
 
 
 Role GCF:
 
 4
 
 
 
 Hiring Manager Name: Chunhai He
 
 
 
 
 About Amgen
 
 
 
 Amgen harnesses the best of biology and technology to fight the world&rsquo;s toughest diseases, and make people&rsquo;s lives easier, fuller and longer. We discover, develop, manufacture and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains on the cutting-edge of innovation, using technology and human genetic data to push beyond what&rsquo;s known today.
 
 
 
 About The Role
 
 
 
 Role Description:
 
 
 We seek a skilled Data Engineer to build and optimize our data infrastructure. As a key contributor, you will collaborate closely with cross-functional teams to design and implement robust data pipelines that efficiently extract, transform, and load data into our AWS-based data lake and data warehouse. Your expertise will be instrumental in empowering data-driven decision making through advanced analytics and predictive modeling.
 
 
 Roles &amp; Responsibilities:
 
 
 <ul>
 <li>
 Building and optimizing data pipelines, data warehouses, and data lakes on the AWS and Databricks platforms.
 </li>
 <li>
 Managing and maintaining the AWS and Databricks environments.
 </li>
 <li>
 Ensuring data integrity, accuracy, and consistency through rigorous quality checks and monitoring.
 </li>
 <li>
 Maintain system uptime and optimal performance
 </li>
 <li>
 Working closely with cross-functional teams to understand business requirements and translate them into technical solutions.
 </li>
 <li>
 Exploring and implementing new tools and technologies to enhance ETL platform performance.
 
 
 </li>
 </ul>
 Functional Skills:
 
 
 
 Must-Have Skills:
 
 
 
 <ul>
 <li>
 Proficient in SQL for extracting, transforming, and analyzing complex datasets from both relational and columnar data stores. Proven ability to optimize query performance on big data platforms.
 </li>
 <li>
 Proficient in leveraging Python, PySpark, and Airflow to build scalable and efficient data ingestion, transformation, and loading processes.
 </li>
 <li>
 Ability to learn new technologies quickly. Strong problem-solving and analytical skills. Excellent communication and teamwork skills.
 
 
 </li>
 </ul>
 
 Good-to-Have Skills:
 
 
 
 <ul>
 <li>
 Experienced with SQL/NOSQL database, vector database for large language models
 </li>
 <li>
 Experienced with data modeling and performance tuning for both OLAP and OLTP databases
 </li>
 <li>
 Experienced with Apache Spark, Apache Airflow
 </li>
 <li>
 Experienced with software engineering best-practices, including but not limited to version control (Git, Subversion, etc.), CI/CD (Jenkins, Maven etc.), automated unit testing, and Dev Ops
 </li>
 <li>
 Experienced with AWS, GCP or Azure cloud services
 
 
 </li>
 </ul>
 Education and Professional Certifications
 
 
 <ul>
 <li>
 Bachelor&rsquo;s degree in computer science and engineering preferred, other Engineering field is considered
 </li>
 <li>
 AWS Certified Data Engineer preferred
 </li>
 <li>
 Databricks Certificate preferred
 
 
 </li>
 </ul>
 Soft Skills:
 
 
 <ul>
 <li>
 Excellent analytical and troubleshooting skills.
 </li>
 <li>
 Strong verbal and written communication skills
 </li>
 <li>
 Ability to work effectively with global, virtual teams
 </li>
 <li>
 High degree of initiative and self-motivation.
 </li>
 <li>
 Ability to manage multiple priorities successfully.
 </li>
 <li>
 Team-oriented, with a focus on achieving team goals
 </li>
 <li>
 Strong presentation and public speaking skills.
 
 
 </li>
 </ul>
 EQUAL OPPORTUNITY STATEMENT
 
 
 Amgen is an Equal Opportunity employer and will consider you without regard to your race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.
 
 
 We will ensure that individuals with disabilities are provided with reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
</div>

Accenture

Project Role : Data Engineer 
Project Role Description : Design, develop and maintain data solutions for data generation, collection, and processing. Create data pipelines, ensure data quality, and implement ETL (extract, transform and load) processes to migrate and deploy data across systems.
 
Must have skills : Google Cloud Platform Architecture 
Good to have skills : NA 
Minimum 3 year(s) of experience is required 
Educational Qualification : 15 years full time education 
 
Summary: As a Data Engineer, you will design, develop, and maintain data solutions that facilitate data generation, collection, and processing. Your typical day will involve creating data pipelines, ensuring data quality, and implementing ETL processes to effectively migrate and deploy data across various systems, contributing to the overall efficiency and reliability of data management within the organization. Roles &amp; Responsibilities: - Expected to perform independently and become an SME. - Required active participation/contribution in team discussions. - Contribute in providing solutions to work related problems. - Collaborate with cross-functional teams to understand data requirements and translate them into technical specifications. - Monitor and optimize data pipelines for performance and reliability. Professional &amp; Technical Skills: - Must To Have Skills: Proficiency in Google Cloud Platform Architecture. - Strong understanding of data modeling and database design principles. - Experience with data integration tools and ETL processes. - Familiarity with data warehousing concepts and technologies. - Knowledge of programming languages such as Python or Java for data processing. Additional Information: - The candidate should have minimum 3 years of experience in Google Cloud Platform Architecture. - This position is based at our Hyderabad office. - A 15 years full time education is required.
 
15 years full time education

TECHBLOCKS

<div><div>Role: Data Engineer
</div><div>Location: New York, NY</div><div></div><div> 
Responsibilities</div><ul><li>Use the latest technology to build data pipelines and integrate machine learning models
</li><li>Build and expand our data platform
</li><li>Develop applications that run on a Google Cloud-based infrastructure. 
</li></ul><div></div> 
<div>Qualifications
</div><ul><li>2+ years of experience in Big Data and Data Engineering, building enterprise-level applications in a public cloud, preferably Google Cloud
</li><li>Experience building cloud-native data pipelines using Python, Apache Airflow, Apache Spark, and Apache Beam.
</li><li>Working knowledge of SQL and data warehousing concepts, including PostgreSQL and BigQuery.
</li></ul><div>Optionally, knowledge of Mongo and AlloyDB is a plus.
</div><ul><li>Comfortable using Azure DevOps or similar CI/CD tools, Git.
</li><li>Experience with TDD, agile software development processes, and collaborating in multi-functional agile teams.
</li><li>Communicate effectively with technical teams and non-technical stakeholders.
</li><li>Bachelor's degree in Computer Science or related engineering discipline, or equivalent combination of education and experience.
</li></ul><div>Optionally, experience building Docker images and deploying them to a Production environment. Integrate Docker container orchestration framework using Kubernetes by creating pods, config Maps, and deployments using Terraform.
</div><ul><li>Optionally, experience with C#, .NET core, and microservices</li></ul></div>

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