Overview
Support clients in designing, building, and optimizing modern data platforms and end-to-end data engineering solutions across cloud and on-premises environments.
What you'll do
- Design and maintain scalable data pipelines and ETL/ELT processes.
- Implement data platforms, lakes, warehouses, and ingestion frameworks.
- Perform data validation, transformation, reconciliation, and performance tuning.
- Support legacy environment migrations to modern cloud architectures.
- Gather requirements and translate business needs into technical solutions.
- Document technical designs, procedures, standards, and architecture decisions.
- Collaborate with analytics, AI, data science, and business intelligence teams.
What you'll need
- 1–3 years of experience in data engineering or a related technical role.
- Bachelor's degree in a relevant technical or quantitative field.
- Hands-on experience with SQL, relational databases, and ETL/ELT development.
- Understanding of data warehousing, dimensional modeling, and data quality.
- Experience with Python, Scala, Java, or another programming language.
- Familiarity with Azure, AWS, Google Cloud, or another cloud platform.
- Strong analytical, communication, documentation, and collaboration skills.
Nice to have
- Experience with Azure Data Factory, Fabric, Databricks, Snowflake, Redshift, BigQuery, Airflow, dbt, or comparable platforms.
- Knowledge of Apache Spark or distributed data processing.
- Exposure to data governance, metadata, master data management, or observability.
- Consulting or client-facing delivery experience.
- Relevant cloud, Databricks, or Snowflake certifications.
Details
- Remote role with approximately 50% travel.
- Full-time position.
- Preferred location is the Northeast U.S.; eligibility is limited to listed U.S. states and Washington, DC.
- Estimated compensation is $75,000–$85,000 USD yearly.
Read the full description and apply on the company’s own careers page.
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