Information Technology_USA - USA_Engineer

Real Soft•Jacksonville, FL

5d•Hybrid

About The Position

The Information Technology team is seeking a Data Engineer to join their team in Atlanta, GA. This role involves defining data requirements, gathering and wrangling large-scale structured and unstructured data, and validating data using various tools. The engineer will support standardization, customization, and ad-hoc data analysis, developing mechanisms to ingest, analyze, validate, normalize, and clean data. Key responsibilities include creating data policies, developing interfaces and retention models, implementing statistical data quality procedures, and supporting data scientists in data sourcing and preparation. The role also involves developing and maintaining data engineering best practices, contributing to insights on data analytics and visualization, and collaborating with data science and business intelligence teams to build data models and pipelines. The engineer will design, implement, and support scalable data infrastructure solutions, build data pipelines, and engage with business teams to gather requirements and design data solutions. This position will mentor junior data engineers, provide data engineering expertise across multiple projects, analyze insights for leadership, and lead a team of data engineers as a key senior contributor.

Requirements

7+ years of overall IT experience
5+ years of experience in a data engineering/ETL role with a track record of manipulating, processing, and extracting value from large datasets
3+ years of experience with Big Data tools/technologies like Hadoop, Spark, Spark SQL, Kafka, Sqoop, Hive, S3, HDFS, or Cloud platforms e.g. AWS, GCP, etc.
3+ years building, testing, and optimizing data ingestion pipelines, architectures, and data sets with Tibco, IBM or others.
Databricks UI, Managing Databricks Notebooks, Delta Lake with Python, Delta Lake with Spark SQL, Delta Live Tables, Unity Catalog.
High-velocity high-volume stream processing with Apache Kafka and Spark Streaming.
Strong SQL skills with ability to write intermediate complexity queries.
ETL experience with PySpark, Spark SQL , IBM Data Stage or similar.
Agile Scrum, Kanban or SAFe experience.
Google Data Engineering Experience Required: 10 & Above

Nice To Haves

Databricks, Python (and/or Scala) and PySpark/Scala-Spark.
Database solutions like Databricks, Teradata, Mainframe, DB2 or BigQuery.
BI Solutions like Spotfire, OAC, Tableau or PowerBI
Azure, AWS Serverless technologies, like, S3, Kinesis/MSK, lambda, and Glue.
Messaging Platforms like Kafka, Amazon MSK & TIBCO EMS or IBM MQ Series.
Strong SQL skills with ability to write intermediate complexity queries
Experience with GIT code versioning software

Responsibilities

Defines data requirements, gather, and wrangle large scale of structured and unstructured data, and validate data by running various data tools in the Data Environment.
Supports the standardization, customization, and ad-hoc data analysis, and will develop the mechanisms to ingest, analyze, validate, normalize and clean data.
Creates data policy and develop interfaces and retention models which requires synthesizing or anonymizing data.
Implements statistical data quality procedures on new data sources, and by applying rigorous iterative data analytics, supports Data Scientists and analytics and insights creation in data sourcing and preparation to visualize data and synthesize insights of commercial value.
Develops and maintains data engineering best practices and contributes to Insights on data analytics and visualization concepts, methods and techniques.
Works closely with the data science and business intelligence teams to develop data models and pipelines for research, reporting, and machine learning.
Design, implement, and support scalable data infrastructure solutions to integrate with multi-heterogeneous data sources, aggregate and retrieve Big Data in a fast and safe mode, curate data that can be used in BI reporting, analysis, machine learning models and ad-hoc data requests.
Build data pipelines that clean, transform, and aggregate data from disparate sources.
Engages with business teams to gather requirements and design data solutions.
Mentors team of more Junior Data Engineers.
Collaborates across multiple projects to provide data engineering expertise across teams.
Analyzes most relevant insights and shares with leadership to provide strategic recommendations for the business.
Lead a team of data engineers and act as a key senior contributor to a data engineering project.