• Experience: Minimum of 15+ years of experience as a Data Architect in complex, large-scale data environments.
• SSIS, Databricks Expertise: Strong hands-on experience working with SSIS, Databricks, including using Apache Spark for data processing and optimization.
• ETL Tools: Proficient with various ETL tools and frameworks such as Informatica, Talend, or SSIS or DataBricks
• Big Data Technologies: In-depth knowledge of big data processing frameworks like Spark, Flink, Hadoop, Kafka, etc.
• NoSQL/MongoDB: Expertise in working with NoSQL databases, especially MongoDB, for large-scale data storage, retrieval, and optimization.
• Gen AI/AIML Models : Familiarity with data consumption.
• Programming Skills: Proficient in SQL , Python or Java, Power shell for building data pipelines.
• SQL and NoSQL Proficiency: Strong knowledge of SQL and experience working with both relational databases and NoSQL databases like MongoDB.
• Data Modeling: Expertise in designing and implementing data models, including OLAP, OLTP, dimensional, and document-based models (NoSQL
• Data Reporting: Experience in integrating with Reporting platforms like Tableau, Qlik, etc
• Data warehousing: Data warehousing is a key part of ETL process, as it stores data from multiple sources in an organized manner and will be needed to build a repeatable ETL workflow to support many different data sources. Experience in building Datawarehouse, Datalakes in Snowflake.
• Redesign and refactor legacy custom ETL processes to reusable ETL workflows that can ingest diverse data sources to normalize data to standard JSON / XML output.
• Data Governance: Knowledge of data governance, security standards, and best practices for managing sensitive data.
• Version Control: Experience with Git or other version control systems for code management.
• Certification: Databricks certification, MongoDB certification, Snowflake SnowPro certification, or other relevant certifications in data engineering, cloud platforms (AWS, Azure or GCP or TKGI), or big data technologies.
• Soft Skills: Strong problem-solving skills, excellent communication, and the ability to work in a collaborative team environment.
• Analytical Mindset: Ability to translate business requirements into scalable, efficient, and reliable ETL solutions.
• Knowledge on AI/ML/GEN AI is desirable
