For the given job description <<As a Solution Architect - Data Engineering, you will work for an American multinational technology company focusing on various technological domains such as artificial intelligence, online advertising, search engine technology, cloud computing, computer software, quantum computing, e-commerce, and consumer electronics. The job requires expertise in Databricks, a cloud-based data engineering platform.
Key responsibilities:Provide technical leadership in a post-sales capacity to guide strategic customers as they design and implement big data projects, ranging from architectural design to data engineering to model deployment.Identify and drive new initiatives that enable customers to turn their data into actionable business value and align these initiatives with their business outcomes for continued success.Architect production-level workloads, including end-to-end pipeline load performance testing and optimization.Deliver tutorials and training to drive community adoption (including hackathons, conference presentations, etc.)Work closely with Engineering and Product Management to drive the direction of the Databricks product.Contribute to Databricks SME community. Required SkillsHands-on and technical expertise with Apache SparkProven experience of design and implementation experience in big data technologies including Hadoop, NoSQL, MPP, OLTP, OLAP5+ years Experience working as either: Software EngineerData Engineer: query tuning, performance tuning, troubleshooting, and debugging Spark andor other big data solutions.Comfortable programming in Python, Scala, or JavaExperience using and designing solutions on cloud infrastructure and services, such as AWS, Azure, or GCPExperience with Development Tools for CICD, Unit and Integration testing, Automation and Orchestration, REST API, BI tools, and SQL Interfaces. E.g. Jenkins.Excellent communication skills and comfortable presenting to strategic stakeholdersleadership.Experience in customer-facing pre-sales, post-sales, technical architecture guidance, or consulting.Passionate about learning new technologies and making customers successfulData ScienceML Engineer experience in model selection, model lifecycle, hyper-parameter tuning, model serving, and deep learning, using tools like MLFlow desired.
 Life at CapgeminiCapgemini supports all aspects of your well-being throughout the changing stages of your life and career. For eligible employees, we offer:Flexible workHealthcare, including dental, vision, mental health, and well-being programsFinancial well-being programs such as 401(k) and Employee Share Ownership PlanPaid time off and paid holidaysPaid parental leaveFamily-building benefits like adoption assistance, surrogacy, and cryopreservationSocial well-being benefits like subsidized backup childelder care and tutoringMentoring, coaching, and learning programsEmployee Resource GroupsDisaster Relief>> the resume: <<Professional SummaryEnergetic Data Engineer in developing robust code for high-volume businesses. Strong decision-maker with 6 years of experience in Data engineering and help firms in designing and executing solutions for complex business problems involving large scale data warehousing, real-time analytics and reporting solutions. Ability to translate business questions and concerns into specific quantitative questions that can be answered with available data using sound methodologies.
SkillsPython 3.x, R, SQLHadoop, Apache SparkHive, Pig, Kafka, Sqoop, OozieTeradata, SnowflakeAmazon S3,EMR,LambdaGit, Jenkins, SplunkMS OfficeMicrosoft Visual C#.NET
Work HistoryData Engineer,04/2020toCurrentAvanade–Bangor,ME,Combining data from multiple source systems (Profile, Systematics, etc..) and multiple platforms (Snowflake, OneLake, Hubs) and computing canonical gold-star metrics “once and for all” to cut operational costs.Develop Spark jobs to transform data and apply business transformation rules to load/process data across enterprise and application specific layers.Experience in building/operating/maintaining fault tolerant and scalable data processing integrations using AWS.Configured S3 buckets with various life cycle policies to archive the infrequently accessed data based on requirement.Good working experience on submitting the Spark jobs which shows the metrics of the data which is used for Data Quality Checking.Working on building efficient data pipelines that transform high volume data into a format used for analytical, fraud prevention and ML use cases.Extensively used Splunk Search Processing Language (SPL) queries, Reports, Alerts and Dashboards.Excellent knowledge of source control management concepts such as Branching, Merging, Labeling/Tagging and Integration, with tool like Git.Performing Data Quality checks like row count, schema validation, Hash key validation for all data movement between applications.Data Engineer,08/2019to04/2020Avanade–Burlington,NC,Responsible for designing and developing various analytical solutions for gaining analytical insights into large data sets by ingesting and transforming these datasets in the Big Data environment using technologies like Spark, Sqoop, Oozie, HIVE.Scheduling jobs to automate the process for regular executing jobs worked on using Oozie.Developed Oozie workflow schedulers to run multiple Hive and Pig jobs that run independently with time and data availability.Familiar with data architecture including data ingestion pipeline design, Hadoop information architecture, data modeling and data mining,machine learning and advanced data processing.All the projects which I have worked for are Open Source Projects and has been tracked using JIRA.Parallel copying of files between various clusters using Distcp, Kafka in Hadoop.Data Scientist Intern,09/2018to12/2018Ascend Learning–Memphis,TN,Acquire, clean, integrate, analyze and interpret disparate datasets using a variety of statistical data analysis and data visualization methodologies, reporting and authoring findings where appropriate.Developed Linear Mixed Effects Models for Boston Ed-fi dataset to estimate the teacher contribution to the student test scores.Random intercept model which utilized a pre-post-test design and included fixed effects for student demographics and a growth model where students were nested by time (random slope and intercept) and teacher was tested.Generated various Clustering models for entire West Virginia department of education and evaluated cluster’s performance.Big Data Engineer,01/2014to07/2017Verizon–,,Identify customer's digital analytical needs and engage with customer and principal architects daily, understand the business requirements for big data analytical solutions and break down large scale requirements into detailed system specifications.Moving data to HDFS framework using SQOOP from Teradata, SQL Server.Good working experience on Hadoop tools related to Data warehousing like Hive, Pig and also involved in extracting the data from these tools on to the cluster using Sqoop.Solved performance issues in Hive with understanding of joins, groups, bucketing, partitions and working on them using HiveQL.Deployed programs written in PySpark to run Spark MLlib, for analytics and reduced customers churn rate by 25%.
EducationMaster of Science:Business Analytics,Expected in4 2020toThe University Of Texas At Dallas-Richardson,TXGPA:
SkillsPython 3.x, R, SQLHadoop, Apache SparkHive, Pig, Kafka, Sqoop, OozieTeradata, SnowflakeAmazon S3,EMR,LambdaGit, Jenkins, SplunkMS OfficeMicrosoft Visual C#.NET>>. The result is, Potential Fit

Fit assessment: Potential Fit