associate data architect (1058)

technical design.
 Maintain and optimize the data infrastructure required for accurate extraction, transformation, and
loading of data from a wide variety of data sources.
 Design, build and maintain batch or real-time data pipelines in production. 
 Develop ETL/ELT Data pipeline (extract, transform, load) processes to help extract and manipulate
data from multiple sources. 
 Automate data workflows such as data ingestion, aggregation, and ETL processing and should
have good experience with different types of data ingestion techniques: File-based, API-based,
streaming data sources (OLTP, OLAP, ODS etc) and heterogeneous databases.
 Prepare raw data in Data Warehouses into a consumable dataset for both technical and non-
technical stakeholders. 
 Strong experience and implementation of Data lakes, Data warehousing, Data Lakehousing
architectures.
 Ensure data accuracy, integrity, privacy, security, and compliance through quality control
procedures.
 Monitor data systems performance and implement optimization strategies.
 Leverage data controls to maintain data privacy, security, compliance, and quality for allocated
areas of ownership. 
 Experience of AWS tools (AWS S3, EC2, Athena, Redshift, Glue, EMR, Lambda, RDS, Kinesis,
DynamoDB, QuickSight etc.).
 Strong experience with Python, SQL, pySpark, Scala, Shell Scripting etc.
 Strong experience with workflow management & Orchestration tools (Airflow,
 Should hold decent experience and understanding of data manipulation/wrangling techniques.
 Demonstrable knowledge of applying Data Engineering best practices (coding practices to DS, unit
testing, version control, code review).
 Big Data Eco-Systems, Cloudera/Hortonworks, AWS EMR etc.
 Snowflake Data Warehouse/Platform.
 Streaming technologies and processing engines, Kinesis, Kafka, Pub/Sub and Spark Streaming.
 Experience of working with CI/CD technologies, Git, Jenkins, Spinnaker, Ansible etc
 Experience building and deploying solutions to AWS Cloud.
 Good experience on NoSQL databases like Dynamo DB, Redis, Cassandra, MongoDB, or Neo4j
etc. 
 Experience with working on large data sets and distributed computing (e.g.,
Hive/Hadoop/Spark/Presto/MapReduce). ​​
 Good to have working knowledge on Data Visualization tools like Tableau, Amazon QuickSight,
Power BI, QlikView etc.
 Experience in Insurance domain preferred.

experience

6