Data engineer/senior data engineer

1 week ago

Gurgaon, India Cognitio Analytics Inc Full time

Experience: 1 to 8 years - DE/ Sr. DE/ AM /Manager Must Have: SQL Python/ Pyspark Azure Databricks, ADLS Gen 2 Blob Storage, Key Vaults, Bit Bucket, Version control, Azure Dev Ops ETL, Building Data Pipelines, Data Warehousing, Data Modelling and Governance, Data Security Agile Practices, SDLC Data Exploration, Data Quality metrics, Historical data Management, Good problem-solving skill Excellent Comm Skills Exp in Data Engineering domain: 2+ Years. 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 Any kind of health care background eg, - Pharma Azure Data Factory, Cosmos DB, Power BI, Azure Synapse Competitive coding and knows most Py spark syntax by heart. 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. 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 Py Spark 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 Dev Ops 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.

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