Sr Data Engineer

Lennar•Irving, TX

18h•Onsite

About The Position

As a Senior Data Engineer, you are responsible for analyzing large amounts of business data, solving real world problems, and developing metrics and business cases that will enable Business Insights. This is done by leveraging data from various platforms such as Jira, Portal, Salesforce. You will work with a team of Product Managers, Software Engineers and Business Intelligence Engineers to automate and scale the analysis, and to make the data more actionable to manage business at scale. You will own many large datasets, implement new data pipelines that feed into or from critical data systems. A career with purpose. A career built on making dreams true. A career built on building zero defect homes, cost management, and adherence to schedules.

Requirements

Bachelor's degree in Computer Science, Information Technology, or a related field.
6-8 years of experience in data engineering or a related role, with demonstrated success in delivering data solutions.
AWS Glue, Lambda, S3, EC2, CloudWatch, Cloud Trail
Dbt, Snowflake, SQL, Python, Qlik
Proficient in SQL, with the ability to write complex queries, perform query optimization, and conduct performance tuning.
Experience with NoSQL databases, such as MongoDB, Cassandra, or DynamoDB, and an understanding of their appropriate use cases.
Strong programming skills in Python, Java, or Scala, with experience in data processing frameworks (e.g., Apache Spark, Hadoop).
3-5 years of experience with cloud platforms (AWS, Azure, GCP) and data services, such as AWS Redshift, Azure Synapse, or Google BigQuery.
Knowledge of big data technologies, including Hadoop, Spark, Kafka, and HBase, with experience in distributed data processing.
Familiarity with data orchestration tools, such as Apache Airflow for scheduling and managing data workflows.
Experience with data versioning and testing tools, such as Git, DVC (Data Version Control) and dbt (data build tool).
Understanding of data security practices, including encryption, access controls, and data masking.

Responsibilities

Design, implement and support an analytical data infrastructure and working knowledge of Modern Data Warehouse concepts.
Design, build, and maintain efficient and scalable data pipelines and ETL processes to process large volumes of structured and unstructured data.
Optimize data storage and retrieval methods to ensure performance, scalability, and cost-efficiency.
Manage AWS resources including EC2, S3, Glue, Lambda, API’s, IAM, Cloud Watch etc.
Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using SQL and AWS big data technologies
Explore and learn the latest AWS technologies to provide new capabilities and increase efficiency
Collaborate with Data Scientists and Business Intelligence Engineers (BIEs) to recognize and help adopt best practices in reporting and analysis
Help continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers
Maintain internal reporting platforms/tools including troubleshooting and development.
Interact with internal users to establish and clarify requirements in order to develop report specifications.
Work with Engineering partners to help shape and implement the development of BI infrastructure including Data Warehousing, reporting and analytics platforms.
Contribute to the development of the BI tools, skills, culture and impact.
Write advanced SQL queries and Python code to develop solutions.
Working Knowledge of Snowflake.
Collaborate across teams to align AI initiatives with organizational goals and understanding of AI concepts
Knowledge on continuous integration/continuous delivery (CI/CD) pipelines and working on deployments when necessary.