Data Engineer at Bespoke Technologies, Inc.

McLean, Virginia, United States -

Full Time

Start Date

Immediate

Expiry Date

06 May, 26

Salary

0.0

Posted On

05 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, Python, AWS, SQL, Data Pipelines, Data Infrastructure, Data Quality, Data Security, NiFi, Git, Elasticsearch, Kibana, Jupyter Notebooks, NLP, AI, ETL/ELT

Industry

IT Services and IT Consulting

Description

BT-166 – Data Engineer Location: McLean (fully on-site, no remote option) **MUST HAVE A POLY CLEARANCE TO APPLY. Those who do not have a Poly clearance will not be considered.** Bespoke Technologies is seeking a Data Engineer who will provide highly technical and in-depth data engineering support. Key Must Haves The candidate MUST have experience with data engineering, to include designing and building data infrastructure, developing data pipelines, transforming and preparing data, ensuring data quality and security, and monitoring and optimizing systems. The candidate MUST have extensive experience with Python and AWS. Experience with SQL, multi-data source queries with database technologies (PostgreSQL, MySQL, RDS, etc.), NiFi, Git, Elasticsearch, Kibana, Jupyter Notebooks, NLP, AI, and any data visualization tools (Tableau, Kibana, Qlik, etc.) are desired. Required Skills and Demonstrated Experience Demonstrated experience with data engineering, to include designing and building data infrastructure, developing data pipelines, transforming/preparing data, ensuring data quality and security, and monitoring/optimizing systems. Demonstrated experience with data management and integration, including designing and operating robust data layers for application development across local and cloud or web data sources. Demonstrated work experience programming with Python Demonstrated experience building scalable ETL and ELT workflows for reporting and analytics. Demonstrated experience with general Linux computing and advanced bash scripting Demonstrated experience with SQL. Demonstrated experience constructing complex multi-data source queries with database technologies such as PostgreSQL, MySQL, Neo4J or RDS Demonstrated experience processing data sources containing structured or unstructured data Demonstrated experience developing data pipelines with NiFi to bring data into a central environment Demonstrated experience delivering results to stakeholders through written documentation and oral briefings Demonstrated experience using code repositories such as Git Demonstrated experience using Elastic and Kibana technologies Demonstrated experience working with multiple stakeholders Demonstrated experience documenting such artifacts as code, Python packages and methodologies Demonstrated experience using Jupyter Notebooks Demonstrated experience with machine learning techniques including natural language processing Demonstrated experience explaining complex technical issues to more junior data scientists, in graphical, verbal, or written formats Demonstrated experience developing tested, reusable and reproducible work Work or educational background in one or more of the following areas: mathematics, statistics, hard sciences (e.g. Physics, Computational Biology, Astronomy, Neuroscience, etc.) computer science, data science, or business analytics Desired Skills and Demonstrated Experience Demonstrated experience with cloud services, such as AWS, as well as cloud data technologies and architecture. Demonstrated experience using big data processing tools such as Apache Spark or Trino Demonstrated experience with machine learning algorithms Demonstrated experience with using container frameworks such as Docker or Kubernetes Demonstrated experience with using data visualizations tools such as Tableau, Kibana or Apache Superset Demonstrated experience creating learning objectives and creating teaching curriculum in technical or scientific fields

Responsibilities

The Data Engineer will provide highly technical and in-depth data engineering support, focusing on designing and building data infrastructure and developing robust data pipelines. This includes transforming and preparing data while ensuring high levels of data quality and security across systems.