Auxo AI

Role Summary:

AuxoAI is seeking a skilled and experienced Data Engineer to join our dynamic team. The ideal candidate will have 6- 10 years of prior experience in data engineering, with a strong background in AWS (Amazon Web Services) technologies. This role offers an exciting opportunity to work on diverse projects, collaborating with cross-functional teams to design, build, and optimize data pipelines and infrastructure.

Responsibilities:

· Design, develop, and maintain scalable data pipelines and ETL processes leveraging AWS services such as S3, Glue, EMR, Lambda, and Redshift.

· Collaborate with data scientists and analysts to understand data requirements and implement solutions that support analytics and machine learning initiatives.

· Optimize data storage and retrieval mechanisms to ensure performance, reliability, and cost-effectiveness.

· Implement data governance and security best practices to ensure compliance and data integrity.

· Troubleshoot and debug data pipeline issues, providing timely resolution and proactive monitoring.

· Stay abreast of emerging technologies and industry trends, recommending innovative solutions to enhance data engineering capabilities.

Qualifications:

· Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

· 6-10 years of prior experience in data engineering, with a focus on designing and building data pipelines.

· Proficiency in AWS services, particularly S3, Glue, EMR, Lambda, and Redshift.

· Strong programming skills in languages such as Python, Java, or Scala.

· Experience with SQL and NoSQL databases, data warehousing concepts, and big data technologies.

· Familiarity with containerization technologies (e.g., Docker, Kubernetes) and orchestration tools (e.g., Apache Airflow) is a plus.

Job Description :

As a Data & Analytics Architect, you will lead key data initiatives, including cloud transformation, data governance, and AI projects. You'll define cloud architectures, guide data science teams in model development, and ensure alignment with data architecture principles across complex solutions. Additionally, you will create and govern architectural blueprints, ensuring standards are met and promoting best practices for data integration and consumption.

Responsibilities :

- Play a key role in driving a number of data and analytics initiatives including cloud data transformation, data governance, data quality, data standards, CRM, MDM, Generative AI and data science.

- Define cloud reference architectures to promote reusable patterns and promote best practices for data integration and consumption.

- Guide the data science team in implementing data models and analytics models.

- Serve as a data science architect delivering technology and architecture services to the data science community.

- In addition, you will also guide application development teams in the data design of complex solutions, in a large data eco-system, and ensure that teams are in alignment with the data architecture principles, standards, strategies, and target states.

- Create, maintain, and govern architectural views and blueprints depicting the Business and IT landscape in its current, transitional, and future state.

- Define and maintain standards for artifacts containing architectural content within the operating model.

Requirements :

- Strong cloud data architecture knowledge (preference for Microsoft Azure)

- 8-10+ years of experience in data architecture, with proven experience in cloud data transformation, MDM, data governance, and data science capabilities.

- Design reusable data architecture and best practices to support batch/streaming ingestion, efficient batch, real-time, and near real-time integration/ETL, integrating quality rules, and structuring data for analytic consumption by end uses.

- Ability to lead software evaluations including RFP development, capabilities assessment, formal scoring models, and delivery of executive presentations supporting a final - recommendation.

- Well versed in the Data domains (Data Warehousing, Data Governance, MDM, Data Quality, Data Standards, Data Catalog, Analytics, BI, Operational Data Store, Metadata, Unstructured Data, non-traditional data and multi-media, ETL, ESB).

- Experience with cloud data technologies such as Azure data factory, Azure Data Fabric, Azure storage, Azure data lake storage, Azure data bricks, Azure AD, Azure ML etc.

- Experience with big data technologies such as Cloudera, Spark, Sqoop, Hive, HDFS, Flume, Storm, and Kafka.

Role : AI/ML Consultant

Notice period : Immediate to 30days only

Experience : 7 - 11 years

Responsibilities :

- AI/ML Consultants will be responsible to design, develop, and implement AI-driven business copilots that enhance the efficiency and effectiveness of organizations

- They will be involved in data pre-processing, feature engineering, model training, and optimization of machine learning algorithms

- Additionally, they will work on integrating AI copilots into existing systems, analyze data to provide insights, and collaborate with cross-functional teams to understand and address business requirements

- AI/ML Consultants will work closely with clients to understand their requirements, tailor solutions to their specific needs, and provide technical guidance and support throughout the project lifecycle

- Drive continuous improvement initiatives, proactively identifying areas for optimization and innovation to maximize client value.

- Share your expertise and best practices with colleagues, mentoring them & contributing to the growth and development of the AuxoAI team.

Requirements :

- 6+ years of experience in consulting, project management, or a related role, with a strong focus on the healthcare and/or CPG industries.

- Experience with natural language processing (NLP) and/or computer vision (CV) is a plus.

- Knowledge of containerization technologies (e.g., Docker, Kubernetes) and microservices architecture.

- Contributions to open-source projects or publications in relevant conferences/journals.

- Familiarity with DevOps practices for continuous integration and deployment (CI/CD).

- Deep understanding of data analytics, data strategy, and digital transformation methodologies, with hands-on experience in implementing data-driven solutions.

- Familiarity with AI technologies, machine learning algorithms, and natural language processing (NLP) techniques.

We are seeking skilled Data Engineers with prior experience on Azure data engineering services and SQL server to join our team. As a Data Engineer, you will be responsible for designing and implementing robust data infrastructure, building scalable data pipelines, and ensuring efficient data integration and storage.

Experience : 6 - 10 years

Notice : Immediate to 30days

Responsibilities :

- Design, develop, and maintain scalable data pipelines and use Azure Data Factory and Azure Stream Analytics

- Collaborate with data scientists and analysts to understand data requirements and implement solutions that support analytics and machine learning initiatives.

- Optimize data storage and retrieval mechanisms to ensure performance, reliability, and cost-effectiveness.

- Implement data governance and security best practices to ensure compliance and data integrity.

- Troubleshoot and debug data pipeline issues, providing timely resolution and proactive monitoring.

- Stay abreast of emerging technologies and industry trends, recommending innovative solutions to enhance data engineering capabilities.

Qualifications :

- Proven experience as a Data Engineer or in a similar role.

- Experience in designing and hands-on development in cloud-based (AWS/Azure) analytics solutions.

- Expert level understanding on Azure Data Factory, Azure Synapse, Azure SQL, Azure Data Lake, Azure App Service, Azure Databricks, Azure IoT, Azure HDInsight + Spark, Azure Stream Analytics

- Knowledge of Dev-Ops processes (including CI/CD) and Infrastructure as code is essential.

- Experience in SQL server and procedures.

- Thorough understanding of Azure Infrastructure offerings.

- Strong experience in common data warehouse modelling principles including Kimball, Inmon.

- Experience in additional Modern Database terminologies.

- Working knowledge of Python or Java or Scala is desirable

- Strong knowledge of data modelling, ETL processes, and database technologies

- Experience with big data processing frameworks (Hadoop, Spark) and data pipeline orchestration tools (Airflow).

- Solid understanding of data governance, data security, and data quality best practices.

- Strong analytical and problem-solving skills, with attention to detail.