Ganesh Jujjuru

ROLE
• Built a solution architecture for rules and a business glossary within data management encompassing data governance, reference management, and master data management.
• Implemented a solution for achieving "one source of truth" for data and process aspects.
• Utilized a data vault approach to data modeling within Snowflake, integrating source-scoped dimensions with conformed dimensions and incorporating master data management and reference management.
• Established data warehouse and data lake structures, building the enterprise data warehouse for order management in SQL Server and transitioning to the data lake, Snowflake, using ADF and WhereScape as the data ingestion platform.
• Leveraged Fivetran as an accelerator for one-time data transfer until streaming and batch processing were established.
• Employed DBT and ADF for transformation within Azure ADLS, Synapse, and Snowflake to facilitate data movement across Raw, EH, EUH, and curated layers.
• Built custom transformation templates, including Macros and Lift, and shift in DBT modeling, creating materialized incremental views, dimensions, and facts.
• Worked on a customer 360-view, including customer MDM, product MDM, invoices, and customer success.

ROLE
• Implemented a data strategy and governance framework with clearly defined roles and responsibilities.
• Provided thought leadership by addressing business problems and guiding on functional and technical aspects.
• Achieved compliance with the General Data Protection Regulation (GDPR) by collaborating with the information security team.
• Established a metadata catalog, including classification, dependencies, and impact using Alation for data sources, including Azure Synapse, ADF, and Postgres.
• Conducted profiling using native scanners and created customized profiles for Synapse, Cosmos DB, Parquet, and Avro files, driving identical profiling for duplicate remediation.
• Built customized dashboards for business products and enrichment and access policy management via Business Process Model and Notation (BPMN) workflows and ServiceNow.
• Formed talented data science teams that created AI/ML and generative AI data products.
• Estimated the composition of hydrocarbons in oil using genetic algorithms with end-to-end product development using Scala-Spark.
• Implemented predictive maintenance for machines using sensor data.
• Built graph anomaly detection models utilizing tailor-made algorithms for rate engine applications.

ROLE
• Implement a centralized data quality and reconciliation framework.
• Develop a data quality framework using Informatica Data Quality (IDQ) and Python scripts.
• Create a reconciliation framework for the stock-keeping unit (SKU) and customer lifecycle.
• Design conceptual and logical information models for the enterprise and business information architecture.
• Ensure compliance with DW/BI standards and guidelines for developing information models and database designs.
• Collaborate with the DBA to translate the logical information model into a preliminary logical database design or a physical information model for the target DBMS.
• Generate DDL and DML scripts to load data into BigQuery from GCS buckets.
• Formulate ETL and ELT strategies using SQL scripts to load data into BigQuery, leveraging GCP Composer airflow.
• Establish BigQueryz data sets, tables, and pipelines for storing processed results, configuring services storage, and BigQuery using Cloud Shell in GCP.
• Develop audit tables for reconciliation and metadata tracking.
• Utilize Airflow to orchestrate workflows between internal components in GCP.
• Use the Dataplex data governance tool to establish data lineage for all Composer and Data Fusion pipelines.

ROLE
• Identify point-to-point integrations, API gateways, and downstream analytics within the existing architecture during discussions with business analysts, subject matter experts, architects, and project owners.
• Design the data lake in Snowflake and the landing zone in AWS S3.
• Create data ingestion processes with reusable frameworks.
• Develop data ingestion pipelines utilizing various AWS cloud services, including Lambda, Step Functions, CloudWatch Events, Simple Notification Service (Amazon SNS) notifications, S3, EC2, Python Boto3 SDK, Athena queries, IAM roles, policies, AWS Glue, and notebooks.
• Gained expertise in Unified Data Analytics with Databricks, managing the Databricks workspace UI, Databricks Notebooks, Delta Lake with Python, and Delta Lake with Spark SQL.
• Design and develop Spark programs/code using Scala, Java APIs, Hive, and HBase.
• Work in cloud technologies, specializing in designing data lakes in Snowflake and MongoDB.
• Create Hive queries and Pig scripts for data analysis, transfer, and table design.
• Develop data pipelines and real-time streaming using Kafka, Spark Streaming, and Flume, including topics and producers.
• Implement MapReduce jobs using Java APIs and Pig Latin.

ROLE
• Develop reusable data pipelines with Azure Data Factory (ADF) and Databricks for delta processing across domains, ensuring code consistency.
• Organize Azure Data Lake Storage (ADLS) directories within BDAT domains and banners.
• Reorganize ADF pipelines and Databricks code based on the ADLS layer.
• Migrate pipelines for the curation layer and configure batch process parameters for bronze layer ingestion and silver layer processing using reusable code.
• Implement data quality metrics at Hive Metastore (HMS) during the data transfer from the bronze to the silver layer using DQ Soda and Databricks.
• Establish partner mobile device management (MDM) and customer master data management (MDM) to monitor the customer lifecycle from campaign to churn usage.
• Utilize Profisee MDM and configure Purview scans on ADLS, ADF, Databricks, PowerBI, Soda DQ, and Profisee MDM.
• Address Purview limitation issues related to lineage using Atlas APIs, including duplicate asset creation, unresolved names, view lineage, DDL, and PowerBI scanner.
• Customize workflow templates for self-read access to governance interface sources for access management.

ROLE
• Identify the integration's data fabric and ontology and close the gaps for real-time and batch integration using Informatica Intelligent Cloud Services (IICS), application integration, data integration, and mass ingestion for one-time loading.
• Align the roadmap of extract, transform, and load (ETL) and extract, load, and transform (ELT) and streaming using multiple statements of work (SOWs) on proof of concept for the technology stack of Fivetran, including Kafka and Azure Data Factory (ADF).
• Discover the resource skills required to fill them with the competent skills.
• Onboard the resources at the client end from 1 to 9 associates within 6 months, manage deliverable tracking, and guide the technical team.
• Identify the enterprise architecture roadmap, design the governance strategy, and create the technical and business model framework.
• Provide a platform for various applications by transforming and sharing financial messages of different formats.
• Identify the business stakeholders and approvers for the asset changes and data models.
• Capture the business glossary and hierarchical terms on the business and technical end.
• Categorize domains using BDAT terminology and identify sensitive data PII and non-PII.