Data Engineer

Edgesource Corporation•McLean, VA

12d•$130,000 - $145,000•Hybrid

About The Position

We are seeking a Data Engineer to work with a small team to build complex data flows for a custom application. Successful candidate will have advanced Python programming skills, familiarity with Java, an understanding of data security, privacy, governance and compliance principles and a demonstrated history of building production data pipelines and ETL workflows at scale.

Requirements

Active TS/SCI Full Scope Polygraph
Minimum of 5 years' experience
Demonstrated experience building production data pipelines and ETL/EL workflows at scale
Proficiency with Apache Spark and PySpark for distributed data processing
Advanced Python programming skills including data manipulation libraries (Pandas, NumPy) and data engineering best practices
Understanding of data security, privacy, governance, and compliance principles
Experience with workflow orchestration tools (such as Step Functions, Airflow)
Familiarity with containerization (such as Docker or Podman) and deploying data applications in cloud environments
Experience with AWS services (S3, Lambda, Step Functions)
Experience with PostgreSQL and MySQL in production environments, including performance tuning and schema design
Demonstrated experience with SQL and query optimization for complex analytical workloads
Experience with version control (Git) and Cl/CD practices for data pipelines
Demonstrated ability to work with stakeholders to understand data requirements, assess feasibility, and design appropriate solutions with minimal oversight
Strong problem-solving and debugging skills for data quality issues, pipeline failures, and performance bottlenecks

Nice To Haves

Experience with data Lakehouse architecture using Apache Iceberg
Hands-on experience configuring, deploying, and integrating data platform components: Apache Ranger (access control and data governance), Trino (distributed SQL query engine), Data catalogs (Unity Catalog OSS, Apache Polaris, etc.), Apache Superset data visualization and dashboarding)
Proficiency with Bash scripting for automation and data processing tasks
Experience with Infrastructure as Code (Terraform or CloudFormation) for data infrastructure
Familiarity or experience with tracking data lineage and associated tooling such as Open lineage
Familiarity or experience with Java
Familiarity with data quality frameworks, testing methodologies, and validation strategies
Background with large-scale data migrations or platform modernization efforts
Experience integrating Al/ML services and models (translation, OCR, speech-to-text, NLP, language detection, topic modeling), LLMs, and RAG retrieval-augmented generation) pipelines
Familiarity with geospatial data processing H3, PostGIS, or similar)
Contributions to data engineering documentation, best practices, and design patterns
Experience with NoSQL databases (DynamoDB, etc.)

Responsibilities

Building end-to-end data pipelines leveraging Python
Using orchestration tools to deploy data pipelines, including configuring and updating Spark Jobs
Containerizing and deploying applications in cloud environments like AWS.
Working with MySQL and PostgreSQL including performance tuning, schema design, and query optimization for complex, analytical workloads.
Leveraging industry standard tools for code control (Git, IaaC control, etc.)
Working with data catalogs, tracking data lineage and handling a variety of data formats, including Geospatial.
Using Bash scripting for automation and data processing tasks
Integrating Al/ML services and models
Work with stakeholders to understand data requirements, assess feasibility, and design appropriate solutions with minimal oversight
Leverage strong problem-solving and debugging skills for data quality issues, pipeline failures, and performance bottlenecks
Leverage a background in large-scale data migration or platform modernization efforts
Contribute to data engineering documentation, best practices, and design patterns.