Job description

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

Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Job description

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