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Data Engineer at Weekday AI. This role is for one of Weekday’s clients. Min Experience: 5 years. Location: Remote (India). JobType: full-time. REQUIREMENTS. . Proficient in . . . . Programming language. : Python, PySpark , Scala . . . Azure Environment: . Azure Data Factory, Databricks, Key Vault, DevOps CI CD. . . Storage/ Databases: . ADLS Gen 2, Azure SQL DB, Delta Lake. . . Data Engineering: . Apache Spark, Hadoop, optimization, performance tuning, Data modelling. . Experience working with data sources such as Kafka and MongoDB is preferred.. . . Experience with Automation of Test Cases of Big Data & ETL. . Pipelines and Agile Methodology. . Basic Understanding of ETL Pipelines. . A strong understanding of AI, machine learning, and data science concepts is highly beneficial.. . Strong analytical and problem-solving skills with attention to detail.. . Ability to work independently and as part of a team in a fast-paced environment.. . Excellent communication skills, able to collaborate with both technical and non-technical stakeholders.. . Experience designing and implementing scalable and optimized data architectures followed by all best practices.. . Strong understanding of data warehousing concepts, data lakes, and data modeling.. . Familiarity with data governance, data quality, and privacy regulations.. .  . Key Responsibilities:. . . Data Pipeline Development:. Design, develop, and maintain scalable and efficient data pipelines to collect, process, and store data from various sources (e.g., databases, APIs, third-party services).. . . Data Integration:. Integrate and transform raw data into clean, usable formats for analytics and reporting, ensuring consistency, quality, and integrity.. . . Data Warehousing:. Build and optimize data warehouses to store structured and unstructured data, ensuring data is organized, reliable, and accessible.. . . ETL Processes:. Develop and manage ETL (Extract, Transform, Load) processes for data ingestion, cleaning, transformation, and loading into databases or data lakes.. . . Performance Optimization:. Monitor and optimize data pipeline performance to handle large volumes of data with low latency, ensuring reliability and scalability.. . . Collaboration:. Work closely with other product teams , TSO and business stakeholders to understand data requirements and ensure that data infrastructure supports analytical needs.. . . Data Quality & Security:. Ensure that data systems meet security and privacy standards, and implement best practices for data governance, monitoring, and error handling.. . . Automation & Monitoring:. Automate data workflows and establish monitoring systems to detect and resolve data issues proactively.. . Understand the broad architecture of the GEP's entire system as well as Analytics.. . Take full accountability for role, own development and results. . Company Location: India.