For the given job description <<Allergan Data Labs is on a mission to transform the Allergan Aesthetics beauty business at AbbVie, one of the largest pharmaceutical companies in the world. Our iconic brands include BOTOX Cosmetic, CoolSculpting, JUVDERM and more.

Allergan Data Labs is on a mission to transform the Allergan Aesthetics beauty business at Abbvie, one of the largest pharmaceutical companies in the world. Allergan Aesthetics brands include Botox, CoolSculpting, Juvderm and many more. The medical aesthetics business is ripe for disruption and we're building a high performing Data Science and Engineering team to do just that.

Our team has successfully launched a new and innovative technology platform, All, which serves millions of consumers, tens of thousands of aesthetics providers, and thousands of colleagues throughout the US. Since its launch in November 2020, All has delivered curated promotions, personalized experiences, and millions of consumers use it as part of their beauty journey.

We're looking for a Senior Data Engineer interested in working within a startup-oriented environment while having the backing of a very large company. If that's you, please read on.

You Will
Work with cross functional partners - Data Scientists, Engineers, Product Managers to understand and deliver data needs.Champion code quality, reusability, scalability, security and help make strategic architecture decisions with the lead engineerDesign, build and launch extremely efficient and reliable data pipelines to move data across a number of platforms including Data Warehouse, online caches and real-time systems.Build product-focused datasets and scalable, fault-tolerant pipelines.Build data quality checks, data anomaly detection, and optimize pipelines for ideal compute and storage.Develop APIs and Microservices to expose and integrate data products with software systems
Required Experience & Skills
5+ years of experience as a Data Engineer writing code to extract, ingest, process and store data within SQL, NoSQL and MPP databases like SnowflakeStrong development experience with Python (or ScalaJava)Experience with complex SQL, batch and streaming pipelines with Apache Spark framework.Knowledge of relational and dimensional data modelingExperience with data quality checks, data validation and data anomaly detection.Experience with workflow management engines like AirflowExperience with Git, CICD pipelines, Docker, KubernetesExperience with architecting solutions on AWS or similar public clouds.Experience with Microservices and Event Driven Architecture
Additional Desired Competencies
Experience with offline and online feature engineering solutions for Machine LearningPrevious domain knowledge in the digital marketingExperience with API development to expose data productsExperience with building Data and ML platforminfrastructure
Perks
Competitive salary.Competitive annual bonus targets.401k with dollar-for-dollar match, up to 6% of eligible earnings (base, bonus). Plus, additional company contribution.RSU grants (Long Term Incentives) for approved roles.Comprehensive medical, dental, vision and life insurance.17 paid holidays per year, including 3 floating holidays.Annual Paid Time Off (PTO), with separate sick daysGenerous discounts on SkinMedica skin care products.Discounted aesthetic treatment days multiple times a year.$600 worth of Alle benefits each year to use towards aesthetic treatments and products.Eligible for donation matching to over 1.5 million nonprofit organizations.
Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law:
The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location, and we may ultimately pay more or less than the posted range. This range may be modified in the future.We offer a comprehensive package of benefits including paid time off. (vacation, holidays, sick), medicaldentalvision insurance and 401(k) to eligible employees.This job is eligible to participate in our short-term incentive programs.This job is eligible to participate in our long-term incentive programs.
Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested and determinable. The amount and availability of any bonus, commission, incentive, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole and absolute discretion unless and until paid and may be modified at the Company's sole and absolute discretion, consistent with applicable law.

Compensation Range (Minimum - Maximum)

$109,500$208,000 USD

Core Values
Be Humble: You're smart yet always interested in learning from others.Work Transparently: You always deal in an honest, direct and transparent way.Take Ownership: You embrace responsibility and find joy in having the answers.Learn More: Through blog posts, newsletters, podcasts, video tutorials and meetups you regularly self educate and improve your skill set.Show Gratitude: You show appreciation and return kindness to those you work with.
Perks For Our Full Time Employees
Competitive salary.Competitive annual bonus targets.401k with dollar for dollar match, up to 6% of eligible earnings (base, bonus). Plus additional company contribution.RSU grants (Long Term Incentives) for approved roles.Comprehensive medical, dental, vision and life insurance.17 paid holidays per year, including 3 floating holidays.Annual Paid Time Off (PTO), with separate sick days12 weeks paid Parental LeaveCaregiver LeaveAdoption and Surrogacy Assistance PlanFlexible workplace accommodations.We celebrate our wins with opportunities to attend Lakers, Knicks, Anaheim Ducks, Anaheim Angels and NY Rangers games.Opportunities to attend concerts, festivals and other live entertainment events in recognition of delivering great work.Tuition reimbursement.Attend a tech or marketing conference of your choice each year.A MacBook Pro and accompanying hardware to do great work.A modern productivity toolset to get work done: Slack, Miro, Loom, Lucid, Google Docs, Atlassian and more.Generous discounts on SkinMedica skin care products.Discounted aesthetic treatment days multiple times a year.$600 worth of Alle benefits each year to use towards aesthetic treatments and products.Eligible for donation matching to over 1.5 million nonprofit organizations.Attend AWS Re:Invent in person (Las Vegas) or virtually each year (for certain roles)>> the resume: <<SummaryDynamic and motivated IT professional with around 7 years of experience as a Big Data Engineer with expertise in designing data intensive applications using Hadoop Ecosystem , Big Data Analytical , Cloud Data engineering , Data Warehouse / Data Mart, Data Visualization , Reporting , and Data Quality solutions .In - depth knowledge of Hadoop architecture and its components like YARN , HDFS, Name Node, Data Node, Job Tracker, Application Master, Resource Manager , Task Tracker and Map Reduce programming paradigm.Extensive experience in Hadoop led development of enterprise level solutions utilizing Hadoop components such as Apache Spark, MapReduce, HDFS, Sqoop, PIG, Hive, HBase, Oozie, Flume, NiFi, Kafka, Zookeeper, and YARN.Profound experience in performing Data Ingestion, Data Processing (Transformations, enrichment, and aggregations).Strong Knowledge on Architecture of Distributed systems and Parallel processing, In-depth understanding of MapReduce programming paradigm and Spark execution framework.Experienced with the Spark improving the performance and optimization of the existing algorithms in Hadoop using Spark Context , Spark-SQL , Dataframe API , Spark Streaming, MLlib , Pair RDD 's and worked explicitly on PySpark and Scala .Handled ingestion of data from different data sources into HDFS using Sqoop, Flume and perform transformations using Hive, Map Reduce and then loading data into HDFS.Managed Sqoop jobs with incremental load to populate HIVE external tables. Experience in importing streaming data into HDFS using Flume sources, and Flume sinks and transforming the data using Flume interceptors.Experience in Oozie and workflow scheduler to manage Hadoop jobs by Direct Acyclic Graph ( DAG ) of actions with control flows.Implemented the security requirements for Hadoop and integrating with Kerberos authentication infrastructure- KDC server setup, creating realm /domain, managing.Experience of Partitions, bucketing concepts in Hive and designed both Managed and External tables in Hive to optimize performance .Experience with different file formats like Avro , parquet , ORC , Json and XML .Expertise in Creating, Debugging, Scheduling and Monitoring jobs using Airflow and Oozie.Experienced with using most common Operators in Airflow - Python Operator, Bash Operator, Google Cloud Storage Download Operator, Google Cloud Storage Object Sensor.Hands-on experience in handling database issues and connections with SQL and NoSQL databases such as MongoDB , HBase , Cassandra , SQL server , and PostgreSQL .Created Java apps to handle data in MongoDB and HBase. Used Phoenix to create SQL layer on HBase.Experience in designing and creating RDBMS Tables, Views, User Created Data Types, Indexes, Stored Procedures, Cursors, Triggers and Transactions.Expert in designing ETL data flows using creating mappings/workflows to extract data from SQL Server and Data Migration and Transformation from Oracle/Access/Excel Sheets using SQL Server SSIS .Expert in designing Parallel jobs using various stages like Join, Merge, Lookup, remove duplicates, Filter, Dataset, Lookup file set, Complex flat file, Modify, Aggregator, XML.Hands-on experience with Amazon EC2, Amazon S3, Amazon RDS, VPC, IAM, Amazon Elastic Load Balancing, Auto Scaling, CloudWatch, SNS, SES, SQS, Lambda, EMR and other services of the AWS family.Created and configured new batch job in Denodo scheduler with email notification capabilities and Implemented Cluster setting for multiple Denodo node and created load balance for improving performance activity.Instantiated, created, and maintained CI/CD (continuous integration & deployment) pipelines and apply automation to environments and applications.Worked on various automation tools like GIT, Terraform, Ansible. Experienced in fact dimensional modeling ( Star schema, Snowflake schema ), transactional modeling and SCD (Slowly changing dimension)Experienced with JSON based RESTful web services, and XML/QML based SOAP web services and also worked on various applications using python integrated IDEs like Sublime Text and PyCharm .Efficient Cloud Engineer with years of experience assembling cloud infrastructure. Utilizes strong managerial skills by negotiating with vendors and coordinating tasks with other IT team members. Implements best practices to create cloud functions, applications and databases.
Skills·Big Data Technologies:Hadoop, MapReduce, HDFS, Sqoop, PIG, Hive, HBase, Oozie, Flume, NiFi, Kafka, Zookeeper, Yarn, Apache Spark, Mahout, Sparklib·Databases:Oracle, MySQL, SQL Server, MongoDB, Cassandra, DynamoDB, PostgreSQL, Teradata, Cosmos.·Programming:Python, PySpark, Scala, Java, C, C++, Shell script, Perl script, SQL·Cloud Technologies:AWS, Microsoft Azure·Frameworks:Django REST framework, MVC, Hortonworks·Tools:PyCharm, Eclipse, Visual Studio, SQL*Plus, SQL Developer, TOAD, SQL Navigator, Query Analyzer, SQL Server Management Studio, SQL Assistance, Eclipse, Postman·Versioning tools:SVN, Git, GitHub·Operating Systems:Windows 7/8/XP/2008/2012, Ubuntu Linux, MacOS·Network Security:Kerberos·Database Modelling:Dimension Modeling, ER Modeling, Star Schema Modeling, Snowflake Modeling·Monitoring Tool:Apache Airflow·Visualization/ Reporting:Tableau, ggplot2, matplotlib, SSRS and Power BI·Machine Learning Techniques:Linear & Logistic Regression, Classification and Regression Trees, Random Forest, Associative rules, NLP and Clustering.
Experience01/2022to02/2022AWS Data EngineerDeloitte–Ann Arbor,MI,Designed and setup Enterprise Data Lake to provide support for various uses cases including Analytics, processing, storing and Reporting of voluminous, rapidly changing dataResponsible for maintaining quality reference data in source by performing operations such as cleaning, transformation and ensuring Integrity in a relational environment by working closely with the stakeholders & solution architectDesigned and developed Security Framework to provide fine grained access to objects in AWS S3 using AWS Lambda, DynamoDBSet up and worked on Kerberos authentication principals to establish secure network communication on cluster and testing of HDFS, Hive, Pig and MapReduce to access cluster for new usersPerformed end- to-end Architecture & implementation assessment of various AWS services like Amazon EMR, Redshift, S3Implemented the machine learning algorithms using python to predict the quantity a user might want to order for a specific item so we can automatically suggest using kinesis firehose and S3 data lakeUsed AWS EMR to transform and move large amounts of data into and out of other AWS data stores and databases, such as Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDBUsed Spark SQL for Scala & amp, Python interface that automatically converts RDD case classes to schema RDDImport the data from different sources like HDFS/HBase into Spark RDD and perform computations using PySpark to generate the output responseCreating Lambda functions with Boto3 to deregister unused AMIs in all application regions to reduce the cost for EC2 resourcesImporting & exporting database using SQL Server Integrations Services (SSIS) and Data Transformation Services (DTS Packages)Coded Teradata BTEQ scripts to load, transform data, fix defects like SCD 2 date chaining, cleaning up duplicatesDeveloped reusable framework to be leveraged for future migrations that automates ETL from RDBMS systems to the Data Lake utilizing Spark Data Sources and Hive data objectsConducted Data blending, Data preparation using Alteryx and SQL for Tableau consumption and publishing data sources to Tableau serverDeveloped Kibana Dashboards based on the Log stash data and Integrated different source and target systems into Elasticsearch for near real time log analysis of monitoring End to End transactionsImplemented AWS Step Functions to automate and orchestrate the Amazon SageMaker related tasks such as publishing data to S3, training ML model and deploying it for predictionIntegrated Apache Airflow with AWS to monitor multi-stage ML workflows with the tasks running on Amazon SageMakerEnvironment: AWS EMR, S3, RDS, Redshift, Lambda, Boto3, DynamoDB, Amazon SageMaker, Apache Spark, HBase, Apache Kafka, HIVE, SQOOP, Map Reduce, Snowflake, Apache Pig, Python, SSRS, TableauAssessed organization technology infrastructure and managed cloud migration process.Configured computing, networking and security systems within cloud environment.Implemented cloud policies, managed technology requests and maintained service availability.01/2016to11/2019Data EngineerHoneywell–Huntsville,AL,Worked on Azure Data Factory to integrate data of both on-prem (MY SQL, Cassandra) and cloud (Blob storage, Azure SQL DB) and applied transformations to load back to Azure SynapseManaged, Configured and scheduled resources across the cluster using Azure Kubernetes ServiceMonitored Spark cluster using Log Analytics and Ambari Web UITransitioned log storage from Cassandra to Azure SQL Datawarehouse and improved the query performanceInvolved in developing data ingestion pipelines on Azure HDInsight Spark cluster using Azure Data Factory and Spark SQLAlso Worked with Cosmos DB (SQL API and Mongo API)Develop dashboards and visualizations to help business users analyze data as well as providing data insight to upper management with a focus on Microsoft products like SQL Server Reporting Services (SSRS) and Power BIPerformed the migration of large data sets to Databricks (Spark), create and administer cluster, load data, configure data pipelines, loading data from ADLS Gen2 to Databricks using ADF pipelinesCreated various pipelines to load the data from Azure data lake into Staging SQLDB and followed by to Azure SQL DBCreated Databrick notebooks to streamline and curate the data for various business use cases and also mounted blob storage on DatabrickUtilized Azure Logic Apps to build workflows to schedule and automate batch jobs by integrating apps, ADF pipelines, and other services like HTTP requests, email triggers etcWorked extensively on Azure data factory including data transformations, Integration Runtimes, Azure Key Vaults, Triggers and migrating data factory pipelines to higher environments using ARM TemplatesIngested data in mini-batches and performs RDD transformations on those mini-batches of data by using Spark Streaming to perform streaming analytics in Data bricksEnvironment: Azure SQL DW, Databrick, Azure Synapse, Cosmos DB, ADF, SSRS, Power BI, Azure Data lake, ARM, Azure HDInsight, Blob storage, Apache Spark.Adept in troubleshooting and identifying current issues and providing effective solutions.Managed performance monitoring and tuning while identifying and repairing issues within database realm.Identified key use cases and associated reference architectures for market segments and industry verticals.Designed surveys, opinion polls, and assessment tools to collect data.Tested, validated and reformulated models to foster accurate prediction of outcomes.Created graphs and charts detailing data analysis results.Recommended data analysis tools to address business issues.Developed new functions and applications to conduct analyses.Cleaned and manipulated raw data.Collaborated with solution architects to define database and analytics engagement strategies for operational territories and key accounts.10/2013to12/2015Big Data Engineer / Hadoop DeveloperInfosys Ltd–Irwindale,CA,AnsibleDenodoDenodoCloudWatchAvroPySparkPySparkPySparkMLlibDataframeNiFiNiFiInteracted with business partners, Business Analysts and product owner to understand requirements and build scalable distributed data solutions using Hadoop ecosystemDeveloped Spark Streaming programs to process near real time data from Kafka, and process data with both stateless and state full transformationsWorked with HIVE data warehouse infrastructure-creating tables, data distribution by implementing partitioning and bucketing, writing and optimizing the HQL queriesBuilt and implemented automated procedures to split large files into smaller batches of data to facilitate FTP transfer which reduced 60% of execution timeWorked on developing ETL processes (Data Stage Open Studio) to load data from multiple data sources to HDFS using FLUME and SQOOP, and performed structural modifications using Map Reduce, HIVED eveloping Spark scripts, UDFS using both Spark DSL and Spark SQL query for data aggregation, querying, and writing data back into RDBMS through SqoopWritten multiple MapReduce Jobs using Java API, Pig and Hive for data extraction, transformation and aggregationAvrom multiple file formats including Parquet, Avro, XML, JSON, CSV, ORCFILE and other compressed file formats Codecs like gZip, Snappy, LzoStrong understanding of Partitioning, bucketing concepts in Hive and designed both Managed and External tables in Hive to optimize performanceDeveloped PIG UDFs for manipulating the data according to Business Requirements and also worked on developing custom PIG LoadersDeveloping ETL pipelines in and out of data warehouse using combination of Python and Snowflakes SnowSQL Writing SQL queries against SnowflakeExperience in report writing using SQL Server Reporting Services (SSRS) and creating various types of reports like drill down, Parameterized, Cascading, Conditional, Table, Matrix, Chart and Sub ReportsUsed DataStax Spark connector which is used to store the data into Cassandra database or get the data from Cassandra databaseWrote oozie scripts and setting up workflow using Apache Oozie workflow engine for managing and scheduling Hadoop jobsWorked on implementation of a log producer in Scala that watches for application logs, transform incremental log and sends them to a Kafka and Zookeeper based log collection platformUsed Hive to analyze data ingested into HBase by using Hive-HBase integration and compute various metrics for reporting on the dashboardTransformed tPySpark using AWS Glue dynamic frames with PySpark; cataloged the transformed the data using Crawlers and scheduled the job and crawler using workflow featureWorked on installing cluster, commissioning & decommissioning of data node, name node recovery, capacity planning, and slots configurationDeveloped data pipeline programs with Spark Scala APIs, data aggregations with Hive, and formatting data (JSON) for visualization, and generatiPySparkEnvironment: AWS, Cassandra, PySpark, Apache Spark, HBase, Apache Kafka, HIVE, SQOOP, FLUME, Apache oozie, Zookeeper, ETL, UDF, Map Reduce, Snowflake, Apache Pig, Python, Java, SSRSOnfidentialDeveloped and implemented Hadoop code while observing coding standards.Optimized and tuned Hadoop environments and modified hardware to meet prescribed performance thresholds.Developed new functions and applications to conduct analyses.Created graphs and charts detailing data analysis results.Tested, validated and reformulated models to foster accurate prediction of outcomes.09/2012to10/2013Python DeveloperFiserv–City,STATE,AWS, S3, EC2, LAMBDA, EBS, IAM, Datadog, CloudTrail, CLI, Ansible, MySQL, Python, Git, Jenkins, DynamoDB, Cloud Watch, Docker, KubernetesLeveraged open communication, collective decision-making and thorough reviews to create performant and scalable systems.Worked with server-side and front-end technologies and leveraged common design patterns to code dynamic and user-friendly systems.Implemented new API routes, architected new ORM structures and refactored code to boost application performance.Harnessed version control tools to coordinate project development and individual code submissions.Introduced cloud-based technologies into Python development to expand on-premise deployment options.Wrote clear and clean code for use in projects.Resolved customer issues by establishing workarounds and solutions to debug and create defect fixes.
Education and TrainingExpected in05/2023totoMaster of Science:Data EngineeringGeorge Mason University-Fairfax,VAGPA:Expected in05/2019totoBachelor of Engineering:Computer Networking And TelecommunicationsUniversity of Mumbai-India,GPA:>>. The result is, 

Fit assessment: Good Fit