Data Engineer

MISSION DRIVEN, LLC
Baltimore, MD, United States
1 day ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
7 years minimum
Working hours
Regular working hours

Tech stack

Artificial Intelligence Airflow Amazon Web Services Amazon S3 Apache HTTP Server Continuous Integration Data Architecture Extract Transform Load (ETL) Data Systems Python (Programming Language) NoSQL SQL Databases
+9 more
Parquet Apache Spark Event Driven Architecture Data Lakes Infrastructure Automation Frameworks Information Technology Avro Data Management Data Pipelines

Requirements

  • 7+ years of professional experience in data engineering, cloud engineering, platform engineering or related fields. \n

  • Strong experience with Python, SQL, and Apache Spark \n

  • Hands-on experience building scalable ETL/ELT pipelines and data processing workflows. \n

  • Experience designing and deploying data platforms within AWS. \n

  • Strong knowledge of relational and NoSQL databases, data lakes, and modern data architectures. \n

  • Experience with AWS data orchestration and data platforms such as MWAA/Airflow, Glue, EMR, S3, Redshift, Lambda, and Step Functions \n

  • Experience with CI/CD and Infrastructure as Code practices. \n

  • Bachelor’s degree in Computer Science, Engineering, or a related technical discipline. \n, * Experience with dbt, Apache Iceberg, Parquet, Avro, or ORC.

About the company

Mission-driven digital transformation firm is looking to add a Senior Data Engineer to its growing engineering team in Baltimore, Maryland (open to full-remote). This is an opportunity to grow into a leadership role whilst building modern cloud data platforms and scalable data solutions supporting mission-critical applications. The ideal candidate will be adept with AWS-native data orchestration/platform services such as: MWAA/Airflow, Redshift, Bedrock, Sagemaker, S3, and Glue. And will play a key role in designing and developing data pipelines, data lake technologies, cloud-native architectures, and AI-enabled data solutions while working alongside engineering, infrastructure, and client teams in a collaborative environment.

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