Senior Associate Data Engineering

Publicis Groupe•Irving, TX

4d•$145,289 - $152,553•Hybrid

About The Position

Publicis Sapient is a digital transformation partner helping established organizations get to their future, digitally-enabled state, both in the way they work and the way they serve their customers. We help unlock value through a start-up mindset and modern methods, fusing strategy, consulting and customer experience with agile engineering and problem-solving creativity. United by our core values and our purpose of helping people thrive in the brave pursuit of next, our 20,000+ people in 53 offices around the world combine experience across technology, data sciences, consulting and customer obsession to accelerate our clients' businesses through designing the products and services their customers truly value. Employer : Sapient Corporation Job Title : Senior Associate Data Engineering Job Requisition : 6630.7650.5 Job Location : 6021 Connection Drive, Irving, Texas 75039. Will work from Company Office (Irving, TX) and various unanticipated client sites and Sapient offices nationally; Telecommuting available on a hybrid basis at company discretion. Job Type : Full Time Rate of Pay : $145,288.50 to $152,552.92 per year Actual compensation will be dependent upon, but not limited to, the individual's skills, experience, qualifications, location, and applicable employment laws.

Requirements

Employer will accept a Bachelor's degree in Computer Science, Information Technology or a related field followed by five years of progressive, post-baccalaureate experience in the job offered or five years of progressive, post-baccalaureate experience in any occupation in which the required experience was gained.
Position also requires three years of experience in each of the following:
Process and transform data and big data using Databricks.
Integrate data from on-premises and data hosted inside of a Virtual network using a single platform at an enterprise scale using Azure Data Factory.
Accelerate data transformation and data cleaning with code free Azure Mapping Data Flows.
Extract, load, and transform data and create enterprise data warehouse using Azure Synapse
Gather relevant data to a project, acquire data via web scraping, interacting with the APIs using Python.
Perform real-time, large-scale data processing in a distributed environment using Spark.
Build solutions to provide high throughput, fault-tolerant horizontally scalable data storage and retrieval with a bare minimum of additional functionality using NoSQL databases Cassandra and MongoDB.
Build real-time streaming data pipelines and applications to process and move data using Kafka and Azure Event Hubs.
Develop portable, high performance and secure applications using Java, Spring and REST Api.
Implement Machine Learning models to find data patterns and make accurate data driven predictions.
Store, retrieve, and manipulate transactional data in relational databases including SQL Server, Postgres, Oracle, and Azure Cosmos DB using SQL.

Responsibilities

Develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software.
Design, implement, and maintain data pipelines and workflows using Azure Data Factory and Databricks.
Build and optimize data integration process from various sources such as databases, files, APIs.
Implement data transformation and cleaning processes using tools like Azure Databricks and Azure Synapse Analytics.
Collaborate with data scientists and analysts to understand data requirements and provide them with reliable datasets.
Performance tuning and optimization of data processes for efficient query processing and resource utilization.
Implement monitor, log, and alert mechanisms to ensure the reliability and availability of data pipelines.
Implement data governance policies and procedures to ensure compliance with regulatory requirements and organizational standards.
Design and implement data security measures such as encryption, access controls, and data masking to protect sensitive information.
Participate in data architecture discussions and providing input on data modelling, schema design, and database optimization.
Evaluate and recommend new tools, technologies, and methodologies to improve data engineering capabilities and efficiency.