Depankar Sarkar

• Managed project planning, resource allocation, schedule, status ,project scope and documentation.
• Perform presentations for the Architecture of system design , Project scope , impact analysis, Deliverables efforts & timelines ,project goals and other key metrics for a successful project delivery.
• Lead and mentor a team of 10 members consisting of data engineers , machine learning engineers and devops engineers.
• Perform hands-on development along with team to make it to production for datawarehouse and bigdata use cases.
• Created automated framework for development and perform POCs to implement reusable solutions benefitting time and cost effectiveness.
• Created innovative solutions and frameworks with respect to Azure Cloud for stream and batch data processing using Databricks , Azure devops CI/CD , Azure Data factory , Azure compute resources (VMs & AKS).
• Evaluated and implemented the best data storage/serving solutions for respective use cases for a better cost management.
• Created a solution using Azure Open AI service and Terraform to automate the development activities by elevating the generative AI approach.
• Created Machine learning and Data engineering frameworks using python for a seamless CI/CD mechanism.
• Implemented Data governance and data security for PII records using best of industry frameworks which is stored in Azure storage location in the form of delta parquets files , also managed to create API based solutions for a secured consumption of data for business.
• Worked and created MLOps framework using Databricks and azure ML taking into account for evaluation of ML Models and CI/CD applications for live projects.
• Implemented Disaster recovery mechanism for the high severity application before making it to production.

Intel471 is a Cybersecurity Intelligence company as the product for its customer base. I am currently working as Principal Data Architect to design the best solutions with respect to data engineering using AWS cloud native technologies.

• Evaluate and implement the Cloud technologies with respect to business requirements which is cost efficient , performance oriented and scalable in nature for Bigdata , Datawarehouse, datalake and Machine learning use cases.
• Implementing Framework for Data/Bigdata Engineering solutions (Functional & Technical) for a variety of data sources in AWS using advanced services as Glue , Apache airflow, EMR(Mapreduce) , Spark , Presto , Redshift, RDS , Athena , AWS Lake formation , Lambda , MLflow, Kubernetes (K8s cluster for docker based microservices) , Fargate, Kafka , Kinesis, Elastic/Opensearch, S3 , DMS ,IAM , Security & Network configurations using VPC , Routing , Transit gateway,VPN, directconnect, private/public subnets etc.
• Hands-on writing Python and Scala codes for project specific use cases which can be referred for productionisation by other engineers.
• Identifying the use cases for data processing as Stream, Batch or event driven.
• Created framework for CI/CD of the code base used for migrations.
• Defining the MLops/Data strategy with data scientists and data engineers in order to achieve the full cycle of Feature engineering , Data transformation , compute resource suitability , DevOps for ML Models and containerization in an automated way for serving and processing of data.
• Perform presentations for the Architecture of system design , Project scope , impact analysis, Deliverables efforts & timelines ,project goals and other key metrics for a successful project delivery.
• Working as an Individual Contributor with developers to implement the data platform.
• Perform meetings & Interactions with business stakeholders , Team members and partners to derive the desired deliverables for the projects.
• Leading a team of Data and ML Engineers to collaborate on progress of deliverables and initiatives.

• Creating and reviewing System design Architecture with end to end implementation for platform with best of Security and Scalable framework in place
• Designed Framework for Data/Bigdata Engineering solutions for a variety of data sources in Google Cloud Platform using advanced services like Dataflow (Apache Beam) , Dataproc , Bigquery , Vertex AI ,Google Cloud Functions, Storage , Pub-Sub , Cloud build , Docker , Kubernetes , Cloud Build, Composer (Apache Airflow) , Cloud Compute , DLP, KMS, VPC Peering & IAM Security etc
• Working on extensive Proof of concepts of auto-scaling solutions using serverless features of GCP , Kubernetes for Container management & Pod Scaling features for ML Operations
• Automated DevOps Solutions for CI/CD Processes using GitHub (Actions) , Git ,Jenkins , Terraform etc
• Implemented Datawarehousing Solutions for ETL as well as Reporting using Informatica , Power BI and Tableau.
• Worked for developing and supporting Python framework for Bigdata solutions on real Time Stream and Batch data transformations pipeline using advanced technology in Spark (Python) and Apache Beam which was implemented using advanced techniques of Windowing (Tumbling, sliding) , Triggers etc
• Leading a team of Data Engineers ,Power BI, Tableau developers & ML engineers to implement end to end solution for the platform as data driven architecture.
• Perform presentations for the Architecture of system design , Project scope , impact analysis, Deliverables efforts & timelines ,project goals and other key metrics for a successful project delivery.

• Developed , Maintained and supported the Data warehousing environment for Weatherford client project.
• Worked as a business Intelligence Developer with skills as Informatica (ETL) , OBIEE ,DAC ,Oracle (PL/SQL) etc

• Worked as BI Developer for Credit-Suisse client and the domain was investment banking.
• Supported as an Onsite Developer and Co-Ordinator (Singapore) for the Offshore development team in Pune. It was a Development Project of BASEL Application which was getting migrated from Basel 2 Framework to Basel 3 framework which uses OBIEE ,PL/SQL ,Unix Shell scripting as the technical skillsets .
• Mainly STAR is the application used for the Data warehouse to Data mart conversion using Comet Calculations which is used for end users reporting in OBIEE. This was Investment Banking group project of Credit Suisse using regulatory reporting.

• Created Datawarehouse and Data mart using Informatica ETL Power centre9.1
• Created Mappings using various Transformation as per the complexity and agile changes as per the client requirement
• Created workflows and sessions for the batches which aimed at the completion of the Datawarehouse creation Data Analysis for data warehouses which are used in creation of reports using Informatica as ETL tool and OBIEE as reporting tool.