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Malavika M

Vetted Talent

Malavika M

Vetted Talent

Experienced Azure Data Engineer with a proven track record in the IT industry. Over two years of hands-on experience in designing, implementing, and managing data solutions on Azure. Specialized in data ingestion, transformation, storage, and analytics using Azure services like Data Factory, SQL Database, Databricks, and Synapse Analytics. Skilled at collaborating with cross-functional teams to gather requirements, architect data solutions, and deliver high-quality outcomes.

  • Role

    Data Engineer

  • Years of Experience

    3.8 years

Skillsets

  • Leadership
  • Data Modelling
  • ETL
  • Effective Communication
  • Adaptability
  • Data Visualization
  • Data Integration
  • Data transformation
  • Olap cube
  • Creative thinking
  • Analytical problem solving
  • Curiosity
  • Python - 3.5 Years
  • SQL - 3.5 Years
  • Apache Spark - 3.5 Years
  • BigQuery - 3.0 Years

Vetted For

9Skills
  • Roles & Skills
  • Results
  • Details
  • icon-skill_image
    Senior Data Engineer With Snowflake (Remote)AI Screening
  • 66%
    icon-arrow-down
  • Skills assessed :Azure synapse, Communication Skills, DevOps, CI/CD, ELT, Snowflake, Snowflake SQL, Azure Data Factory, Data Modelling
  • Score: 59/90

Professional Summary

3.8Years
  • Jul, 2021 - Present3 yr 9 months

    Azure Data Engineer

    Slb

Applications & Tools Known

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    Azure Data Factory

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    Microsoft Power BI

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    Data Warehouse

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    SQL

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    Azure Databricks

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    Logic Apps

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    Azure Virtual Machines

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    MicroStrategy

Work History

3.8Years

Azure Data Engineer

Slb
Jul, 2021 - Present3 yr 9 months

    Experienced Azure Data Engineer with a proven track record in the

    IT industry. Over two years of hands-on experience in designing, implementing,

    and managing data solutions on Azure. Specialized in data ingestion,

    transformation, storage, and analytics using Azure services like Data Factory,

    SQL Database, Databricks, and Synapse Analytics. Skilled at collaborating with

    cross-functional teams to gather requirements, architect data solutions, and

    deliver high-quality outcomes.

    Data integration and data transformation (ETL):

    • Responsible for designing and implementing data integration solutions using Azure Data Factory.
    • Developed data transformationETL pipelines to extract, transform, and load data from various sources into a centralized data warehouse with the help of Pyspark and SQL in Azure Databricks and Azure Dataflows.
    • Implemented data freshness checks and automation of monitoring within the ETL pipelines to ensure data accuracy and reliability.
    • Collaborated with cross-functional teams for data integration
    • Collaborated with business stakeholders to understand requirements.
    • Ensured timely delivery of quality solutions.
    • Designed efficient data integration processes.

    Data Modelling and data visualization:

    • Created and maintained OLAP cubes using Azure Analysis Services (AAS) to provide efficient and accurate data analysis for business stakeholders.
    • Created multiple reports for data integrity and data quality checks using Microsoft Power BI.
    • Integrated Azure Analysis Services with Azure Data Factory and other data sources to automate data refreshes and ensure the availability of up-to-date information for reporting and analysis.
    • Implemented row-level security (RLS) and dynamic security filters in AAS to restrict access to sensitive data based on user roles and permissions.

Achievements

  • Provided continuous training and mentorship to the Associate Data Engineers of the year 2022 and 2023.
  • Completed essential courses and mini projects, leading to grade promotion from G08 to G09.
  • Received Reward of Excellence for 2022 as 'Emerging New Comer'

Major Projects

3Projects

Financial Reporting OLAP cube with ETL using Databricks

Schlumberger
Jan, 2023 - Present2 yr 3 months

    Financial report covering all the actual, plan, forecast and DSO data of the company across the globe focusing on automation & standardization.

    Benefits- Single version of truth, Simplified & timely data availability

    Tools: Azure Data Factory, Azure Data Lake Service, Azure Databricks, Azure SQL Warehouse, Azure SQL DB, Azure Virtual Machines, Azure Logic Apps, Power BI, MicroStrategy, SQL.,

    Created an OLAP cube by employing the below processes:

    1. Implemented a robust stage flow to efficiently extract data from diverse sources including SharePoint, MicroStrategy, SQL databases, and Data Lake, orchestrating the loading process into Azure Data Warehouse for centralized storage with the help of the data orchestration tool Azure Data Factory.
    2. Executed Extract, Transform, Load (ETL) processes using Azure Databricks with the help of PySpark and SQL to harmonize and integrate the extracted data. Leveraged the power of Databricks for scalable and parallelized data processing, ensuring consistency, and accuracy by applying relevant business logic during the transformation phase.
    3. Segregated processed data into dimension tables and fact tables, optimizing data organization to facilitate efficient query performance and analysis. Leveraged Azure Databricks for efficient data partitioning and distribution strategies, enhancing query performance and reducing processing times.
    4. Applied advanced data modelling techniques to enhance the structure and integrity of dimension tables and fact tables, ensuring alignment with business requirements and analytical objectives.
    5. Leveraged Azure Analysis Services to construct an OLAP cube, facilitating analysis and enabling exploration of data insights.
    6. Implemented data quality checks and validation procedures throughout the process to maintain data integrity and reliability using Microsoft Power BI.
    7. Collaborated closely with stakeholders to understand analytical requirements and iteratively refine the OLAP cube design to meet evolving business needs.
    8. Documented the entire process, including data sources, transformations, and cube design, to ensure transparency, reproducibility, and knowledge transfer within the team.

Global Cash Balance OLAP cube with ETL using Dataflow

Schlumberger
Mar, 2022 - Dec, 2022 9 months

    Provides visibility into the global daily and month-end GL cash and bank balances across multiple banks accounts worldwide for the Treasury team.

    Tools - Azure Data Factory, Azure Data Flows, Azure Data Lake Service, Azure SQL Warehouse, Azure SQL DB, Logic Apps, SQL, Automation runbook(PowerShell), AAS, Power BI

    Created an OLAP cube by employing the below processes:

    1. Implemented a robust stage flow to efficiently extract data from diverse sources including SharePoint, Oracle, SQL databases, and Data Lake, orchestrating the loading process into Azure Data Warehouse for centralized storage with the help of the data orchestration tool Azure Data Factory.
    2. Executed Extract, Transform, Load (ETL) processes to harmonize and integrate the extracted data, ensuring consistency and accuracy with the help of Azure Dataflows and applied the relevant business logic.
    3. Segregated processed data into dimension tables and fact tables, optimizing data organization and facilitating efficient query performance.
    4. Applied advanced data modeling techniques to enhance the structure and integrity of dimension tables and fact tables, ensuring alignment with business requirements and analytical objectives.
    5. Leveraged Azure Analysis Services to construct an OLAP cube, facilitating analysis and enabling exploration of data insights.
    6. Implemented data quality checks and validation procedures throughout the process to maintain data integrity and reliability using Microsoft Power BI.
    7. Collaborated closely with stakeholders to understand analytical requirements and iteratively refine the OLAP cube design to meet evolving business needs.
    8. Documented the entire process, including data sources, transformations, and cube design, to ensure transparency, reproducibility, and knowledge transfer within the team.

Common Framework for a unified monitoring dashboard

Slb
Jul, 2021 - Dec, 2021 5 months

    Worked on an automated framework for 20 projects with failure and data delay alerts for end to end ETL till OLAP cube refresh, along with a consolidated Power BI dashboard that tracks real-time and historical CIM progress, reducing manual intervention by 90%.

    Tools: Azure Data Factory, Azure Logic Apps, Microsoft Power BI.

Education

  • Computer Science Engineering

    Amrita School Of Engineering

Certifications

  • Az-900 (microsoft certification: azure fundamentals)

  • Dp-900 (microsoft certification: microsoft azure data fundamentals)

  • Pl-300 (microsoft certified: power bi data analyst associate)

Interests

  • Dance
  • Drawing