Senior Data Analytics Engineer (Hybrid) - Data Pipelines

Applied Information Sciences, Inc.; Alexandria, Northern (VA, KY); Hybrid; USD 110,000 - 166,000; Full time

Job summary

Applied Information Sciences, Inc. is seeking a Data Engineer for an AI Engineering contract onsite in Alexandria, VA.

You will develop production-grade data pipelines for government environments, validate data, and establish robust schemas while collaborating with cross-functional teams. Candidates should have a BS in CS, 5+ years in data engineering, strong Python and SQL skills, experience with Pandas, Pandera, Databricks, Spark, and a willingness to work with large, messy data and data

Qualifications

• Bachelor’s Degree in Computer Science (or closely related degree).
• At least 5+ years of experience in Data Engineering.
• Must have experience with Python, pandas, pandera, SQL, Postgres, Databricks, and Spark.
• Must have a strong Python background.
• Must have strong experience working with messy data, disparate data sources, establishing and maintaining schemas, and setting up data validation steps.
• Must be comfortable working with large data workflows (100GB+).
• Must have an Active Secret Clearance.
• Nice to Have Skills: A related certification is preferred but not required.

Responsibilities

• Develop and deploy software into production in government environments.
• Set up data validation steps.
• Establish and maintain schemas.

Skills

Big data concepts

Governance

ML/predictive analytics

Education

Bachelor's degree in Computer Science or related

Tools

Python

Pandas

Pandera

SQL

Postgres

Databricks

Spark

Job description

Applied Information Sciences, Inc. is seeking a Data Engineer for an AI Engineering contract onsite in Alexandria, VA.

You will develop production-grade data pipelines for government environments, validate data, and establish robust schemas while collaborating with cross-functional teams. Candidates should have a BS in CS, 5+ years in data engineering, strong Python and SQL skills, experience with Pandas, Pandera, Databricks, Spark, and a willingness to work with large, messy data and data