Cognitio Analytics, founded in 2013, aims to be the preferred provider of AI / ML driven productivity solutions for large enterprises. The company has received awards for its Smart Operations and Total Rewards Analytics Solutions and is dedicated to innovation, R&D, and creating sustained value for clients. Cognitio Analytics has been recognized as a "Great Place to Work" for its commitment to fostering an innovative work environment and employee satisfaction.

Our solutions include Total Rewards Analytics powered by Cognitio’s Total Rewards Data Factory, The Total Rewards Analytics solutions help our clients achieve better outcomes and higher ROI on investments in all kinds of Total Rewards programs.

Our smart operations solutions drive productivity in complex operations, such as claims processing, commercial underwriting etc. These solutions, based on proprietary capabilities based on AI, advanced process and task mining, and deep understanding of operations drive effective digital transformation for our clients. 

Experience: 3 - 8 years

Job Description:

We are seeking a highly skilled Azure Cloud Data Engineer to join our dynamic team. As an Azure Cloud Data Engineer, you will be responsible for designing, implementing, and managing cloud-based data solutions using Microsoft Azure. The ideal candidate should have a strong background in data engineering, with hands-on experience in Azure services and tools.

Skill Required:

Must Haves:

• Exp in Data Engineering domain: 2+ Years.
• SQL, Python, Py Spark.
• Spark, Distributed Systems.
• Azure Databricks, ADLS Gen 2 Blob Storage, Key Vaults, Azure DevOps.
• ETL, Building Data Pipelines, Data Warehousing, Data Modelling and Governance.
• Agile Practices, SDLC
• Multi years’ experience with Azure-Databricks ecosystem and Py spark.
• Ability to write clean, concise and organized Py spark code.
• Ability to break down the project into executable steps, prepare a DFD and execute the same
• Propose innovative DE solutions to achieve business objectives.
• Quick on his feet, good at tech and logically complex communication
•  Good Knowledge of ADF, Docker /containerization.

Good to Have:

• Event Hubs
• Azure Data Factory, Cosmos DB, Power BI
• Competitive coding and knows most Py spark syntax by heart. 

Essential Job Duties & Responsibilities:

· Set up workflows and orchestration processes to streamline data pipelines and ensure efficient data movement within the Azure ecosystem.

· Create and configure compute resources within Databricks, including All-Purpose and SQL Compute and Job Clusters to support data processing and analysis.

· Set up and manage Azure Data Lake (ADLS) Gen 2 storage accounts and establish a seamless integration with Databricks Workspace for data ingestion and processing.

·   Create and manage Service Principals, key vaults to securely authenticate and authorize access to Azure resources.

·   Utilize ETL (Extract, Transform, Load) techniques to design and implement data warehousing solutions and ensure compliance with data governance policies.

·   Develop highly automated ETL scripts for data processing.

·   Scale infrastructure resources based on workload requirements, optimizing performance and cost-efficiency.

·    Profile new data sources in a different format including CSVs, JSONs etc.

·     Apply problem-solving skills to address complex business and technical challenges, such as data quality issues, performance bottlenecks, and system failures.

·   Demonstrate excellent soft skills and the ability to effectively communicate and collaborate with clients, stakeholders, and cross-functional teams.

·   Implement Continuous Integration/Continuous Deployment (CI/CD) practices to automate the deployment and testing of data pipelines and infrastructure changes.

· Delivering tangible value very rapidly, collaborating with diverse teams of varying backgrounds and disciplines.

· Codifying best practices for future reuse in the form of accessible, reusable patterns, templates, and code bases.

· Manage timely appropriate communication and relationship with clients, partners and other stakeholders.

· Create and manage periodic reporting of project execution status and other trackers in standard accepted formats.

Required Competencies:

·   Strong proficiency in SQL for data manipulation, querying, and optimization.

·    Proficiency in Python and PySpark for data engineering and data processing tasks.

·    Solid understanding of the Microsoft Azure tooling for large-scale data engineering efforts and deployments is highly preferred.

·    Hands-on experience with Azure Databricks, including data ingestion, transformation, and analysis.

·    Ability to set up and manage Azure Data Lake Storage (ADLS) Gen 2 accounts, and familiarity with data lake architecture and best practices.

·    Knowledge of Azure Key Vaults for securely storing and managing cryptographic keys, secrets, and certificates.

• An ‘engineering’ mindset, willing to make rapid, pragmatic decisions to improve performance, accelerate progress or magnify impact.
• Comfort with working with distributed teams on code-based deliverables, using version control systems and code reviews.
• Use of agile and DevOps practices for project and software management including continuous integration and continuous delivery.
• Ability to conduct data analysis, investigation, and lineage studies to document and enhance data quality and access.

·       Proven track record of scaling infrastructure to meet performance and scalability requirements.

·       Strong analytical and problem-solving skills, with the ability to troubleshoot complex technical issues.

·       Direct experience having built and deployed robust, complex production systems.

·       Proficiency in Event Hubs and Azure Data Factory, particularly in the context of data streaming and transfer.

Qualifications:

·       Bachelor’s degree in computer science, Engineering, or a quantitative discipline.

·       2+ years of professional experience as an Azure Cloud Data Engineer or a similar role.

·       Excellent communication skills, both verbal and written.

·       Strong attention to detail and the ability to work in a fast-paced environment.