Data Engineer

About the role

As a Data Engineer, you will make an impact by building and supporting data solutions that enable reliable, scalable, and efficient access to business-critical information. You will be a valued member of the Data & Analytics team and work collaboratively with data analysts, data scientists, business stakeholders, and engineering teams to develop and maintain data pipelines, data platforms, and cloud-based analytics solutions.

In this role, you will:

Develop and maintain ETL/ELT pipelines that ingest, transform, and deliver data from a variety of enterprise sources.
Support the design, optimization, and maintenance of data warehouses, data lakes, and cloud-based data platforms.
Monitor and troubleshoot data pipelines to ensure data quality, accuracy, and operational reliability.
Write and optimize SQL queries for data extraction, validation, integration, and reporting needs.
Collaborate with cross-functional teams to understand business requirements and deliver scalable data solutions.
Work model

We believe hybrid work is the way forward as we strive to provide flexibility wherever possible. Based on this role's business requirements, this is a hybrid position based in Atlanta, GA requiring time in a Cognizant or client office as determined by project and business needs. Regardless of your working arrangement, we are here to support a healthy work-life balance through our various wellbeing programs.

The working arrangements for this role are accurate as of the date of posting. This may change based on the project you're engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations.

What you need to have to be considered

Bachelor's degree in Computer Science, Information Technology, Data Analytics, Engineering, or a related field.
0–3 years of experience in Data Engineering, Database Development, Data Integration, or related disciplines.
Strong knowledge of SQL and relational database concepts.
Basic to intermediate programming experience in Python, Java, Scala, or similar programming languages.
Understanding of ETL/ELT principles, data integration techniques, and data processing methodologies.
Knowledge of data warehousing concepts, dimensional modeling, and data architecture fundamentals.
Familiarity with cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform (GCP).
Understanding of version control tools such as Git.
Strong analytical, problem-solving, communication, and collaboration skills.
These will help you stand out

Experience with data processing frameworks such as Spark, Databricks, or Hadoop.
Familiarity with ETL tools such as Azure Data Factory, SSIS, Informatica, or Talend.
Experience working with databases including SQL Server, PostgreSQL, Oracle, or MySQL.
Exposure to cloud-native data engineering and analytics solutions within Azure environments.
Experience working in Agile development environments, including sprint planning, stand-ups, and code reviews.
Knowledge of data quality, data governance, and automation best practices.