Data Engineer: Scalable Pipelines, Spark & Cloud

Cognizant

Mesa (AZ)

On-site

USD 59,000 - 72,000

Full time

8 days ago

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Benefits offered by this job

Medical/Dental/Vision/Life Insurance

Paid holidays plus Paid Time Off

401(k) plan and contributions

Long-term/Short-term Disability

Paid Parental Leave

Employee Stock Purchase Plan

Job summary

Cognizant in Plano, TX or Teaneck, NJ is seeking a Data Engineer to design, develop, and maintain robust data pipelines for structured, semi-structured, and unstructured data, leveraging Python, Spark, and SQL. You will collaborate with data scientists and business stakeholders to deliver scalable analytics solutions.

The role emphasizes data quality, data modeling, and governance, with cloud exposure across AWS, Azure, and GCP.

Qualifications

Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, or a related field
Good programming skills in Python and SQL
Good problem-solving, analytical, and communication skills
Ability to work collaboratively in a fast-paced environment
Familiarity with data orchestration tools (e.g., Airflow, Prefect)
Exposure to data lake and data warehouse architectures (e.g., Snowflake, Databricks, Big query etc.)
Knowledge of containerization and CI/CD pipelines (e.g., Docker, Kubernetes, GitHub)
Familiarity with data visualization tools
Basic understanding of ETL/ELT concepts, data modeling, and data architecture

Responsibilities

Design, develop, and optimize data pipelines using Python, Spark, and SQL.
Ingest, process, and analyze structured (e.g., relational databases), semi-structured (e.g., JSON, XML), and unstructured data (e.g., text, logs, images) from diverse sources.
Implement data quality checks, validation, and transformation logic to ensure data integrity and reliability.
Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and deliver solutions.
Develop and maintain data models, data dictionaries, and technical documentation.
Monitor, troubleshoot, and optimize data workflows for performance and scalability.
Ensure compliance with data governance, security, and privacy policies.
Support data migration, integration, and modernization initiatives, including cloud-based solutions (AWS, Azure, GCP).
Automate repetitive data engineering tasks and contribute to continuous improvement of data infrastructure.
Knowledge of any of the cloud platforms (AWS, Azure, GCP).
Understanding of data security, encryption, and compliance best practices.