For the given job description <<Minimum qualifications:

Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.Experience analyzing data and creating reports with database query (e.g., SQL) and visualization tools (e.g., Tableau, Dashboards).Experience with one or more general purpose programming languages: Python, CC++, or Java.

Preferred qualifications:

Knowledge of Machine Learning (ML) and Big Data Infrastructure.Excellent communication and presentation skills.

About The Job

The Business Strategy & Operations organization provides business critical insights using analytics, ensures cross functional alignment of goals and execution, and helps teams drive strategic partnerships and new initiatives forward. We stay focused on aligning the highest-level company priorities with effective day-to-day operations, and help evolve early stage ideas into future-growth initiatives.

The Google Nest team focuses on hardware, software, and services for the home, ranging from Nest thermostats to Nest smart displays. The Google Nest team develops, designs, and develops new technologies and hardware to make users homes more helpful. Our mission is the helpful home: to create a home that cares for the people inside it and the world around it.

The US base salary range for this full-time position is $115,000-$169,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .

Responsibilities

Collaborate with Google Home App (GHA), Home Graph and other Platform engineering team to ensure infrastructure supports key analyses by identifying logging gaps. Build or Modify newexisting data pipelines for tracking GHA metrics.Conduct end-to-end analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.Act as a thought partner to produce insights and metrics for various technical and business stakeholders across the Smart Home Platforms teams.Deliver presentations of findings and recommendations to leadership, creating visual displays of quantitative information.Make business recommendations with the presentations of findings at multiple levels of stakeholders through visual displays of quantitative information.

Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form .>> the resume: <<SummaryOver 3 years of professional IT experience inData EngineeringandData Analyticsusing various languages and tools likeSQL, Python, Big/HadoopExtensive experience on Data Engineering field including Ingestion, Datalake, Datawarehouse, Reporting and Analytics.Strong knowledge and experience on Data Analysis, Data Lineage, Big Data pipelines, Data quality, Data Reconciliation, Data transformation rules, Data flow diagram including Data replication, Data integration and Data orchestration toolsKnowledge and experience on AWS services like Redshift,Redshift spectrum,S3,Glue,Athena, Lambda,cloudwatch and EMRs like HIVE, PrestoExtensively used ETL methodology for performing Data Migration, Extraction, Transformation and loading usingTalendand designed data conversions from wide variety of source systems.Experienced in Data Ingestion projects to inject data intoData lakeusing multiple sources systems using Talend Bigdata.Good technical Skills inSQL Server,ETLDevelopment usingInformatica tool.Expertise in writingSQL,PL/SQLto integrate of complex OLTP and OLAP database models and data marts, worked extensively onOracle, SQL SERVER.Experience in all the life cycle phases of the projects onlarge data setsand experience with performancetuningandtroubleshooting.Ability to work effectively with associates at all levels within the organization.Strong background in mathematics and have very good analytical and problem-solving skills.
SkillsBig Data Ecosystems:-HDFS, MapReduce, Spark, Kafka, Hive, Airflow, Stream Sets, HBase, Flume, Zookeeper, Nifi, Sentry, Ranger.Scripting Language:-Python, PowerShell Scripting, Pig Latin, HiveQL.Cloud Environment:-Amazon Web Services (AWS), Microsoft Azure,NoSQL DatabaseDatabase:-MySQL, Oracle, Teradata, MS SQL SERVER, PostgreSQL, DB2.Version Control:-Git, SVN, Bitbucket.ETL Tools:-Tableau, Microsoft Excel, Informatica, Power BI, R, Google Data Studio.
ExperienceData Engineer,01/2022-CurrentHoneywell–Michigan,ND,Worked on Architecture Design for Multistate implementation or deployment.Implement One time Data Migration of Multistate level data from SQL server to Snowflake by using Python and SnowSQL.Day to-day responsibility includes developing ETL Pipelines in and out of data warehouse, develop major regulatory and financial reports using advanced SQL queries in snowflake.Stage the API or Kafka Data(in JSON file format) into Snowflake DB by FLATTENing the same for different functional services.Build Docker Images to run airflow on local environment to test the Ingestion as well as ETL pipelines.Building/Maintaining Docker container clusters managed by Kubernetes. Utilization of Kubernetes and Docker for the runtime environment of the CI/CD system to build, test and deploy.Created Airflow DAGs to schedule the Ingestions, ETL jobs and various business reports.CreatedAirflowScheduling scripts in Python.xCluster capacity planning along with operations team and management team and Cluster maintenance as well as creation and removal of nodes,HDFSsupport and maintenance.Strong knowledge ofRack awarenesstopology in theHadoop cluster.Involved in Loading data fromLINUX file systemtoHadoop Distributed File System.Responsible for building scalable distributed data solutions usingHadoop.Experience in managing and reviewingHadoop log files.Data migration fromRDMStoHadoopusingSqoopfor analysis and implementedOoziejobs for automatic data imports from source.CreatedHBasetables to store various data formats ofPIIdata coming from different portfolios.Strong in Exporting the analyzed and processed data to theRelational databasesusingSqoopfor visualization and for generation of reports for the team.Data Engineer,01/2021-12/2021Honeywell–Nashville,TN,Prepared ETL design document which consists of the database structure, change data capture, Error handling, restart and refresh strategies.Worked with different feeds data like JSON, CSV, XML,DAT and implemented Data Lake concept.Developed Informatica design mappings using various transformations.Designing and building multi-terabyte, full end-to-end Data Warehouse infrastructure from the ground up on ConfidentialRedshiftfor large scale data handling Millions of records every day.Optimizing and tuning theRedshiftenvironment, enabling queries to perform up to 100x faster for Tableau and SAS Visual Analytics.Wrote various data normalization jobs for new data ingested intoRedshift.Advanced knowledge on ConfidentialRedshiftand MPP database concepts.Migrated on premise database structure to ConfidentialRedshiftdata warehouseDeveloped UDF in Scala to implement the business logic.Developed spark applications in Scala on distributed environment to load huge number of JSON files with different schema in to Hive tables.Most of the infrastructure is on AWS, used,AWS EMRDistribution for HadoopAWS S3for raw file storageAWS EC2for KafkaUsedAWS Lambdato perform data validation, filtering, sorting, or other transformations for every data change in a DynamoDB table and load the transformed data to another data store.CreatedAirflowScheduling scripts in Python.ProgrammedETL functionsbetween Oracle and Amazon Redshift.SQL Developer,06/2019-12/2023Ihs Markit–Southfield,MI,Gathered business requirements and converted them into new T-SQL stored procedures in visual studio for database project.Performed unit tests on all code and packages.Analyzed requirement and impact by participating in Joint Application Development sessions with business client online.Performed and automated SQL Server version upgrades, patch installs and maintained relational databases.Performed front line code reviews for other development teams.Modified and maintained SQL Server stored procedures, views, ad-hoc queries, and SSIS packages used in the search engine optimization process.Updated existing and created new reports using Microsoft SQL Server Reporting Services. Team consisted of 2 developers.Created files, views, tables and data sets to support Sales Operations and Analytics teamsMonitored and tuned database resources and activities for SQL Server databases.
EducationMaster of Science:Data Analytics Engineering,Expected in12/2022-George Mason University-Fairfax,VAGPA:Status-3.8 GPABachelor of Science:Electronics & Communication Engineering,Expected in06/2019-KL University-Guntur,GPA:Status->>. The result is, 

Fit assessment: Good Fit