Data Engineer (Early-Career to Mid-Level)

Job Description

• The Data Engineer (with Upskilling) supports the design, development, and maintenance of data pipelines, data warehouses, and analytics infrastructure.
• This role is ideal for early-career or junior–mid-level professionals who possess foundational data engineering skills and are eager to grow through training, mentorship, and hands-on project exposure.
• The position is fully remote within the USA, with opportunities to advance skills in cloud platforms, big data tools, automation, and modern data engineering frameworks.

Key Responsibilities

Data Pipeline Development

• Assist in building, maintaining, and improving ETL/ELT pipelines for ingestion, transformation, and processing of structured and unstructured data.
• Collaborate with senior data engineers to optimize data flows and ensure scalability and reliability.
• Support integration of data from APIs, databases, third-party sources, and internal systems.

Data Warehousing & Modeling

• Help develop and maintain data models and schemas for analytical and operational use cases.
• Support data warehouse development on platforms such as Snowflake, BigQuery, Redshift, or Azure Synapse.
• Participate in performance tuning and data modeling tasks under guidance.

Data Quality & Governance

• Conduct data validation, profiling, and quality checks to ensure accuracy, completeness, and consistency.
• Assist in implementing best practices for data governance, metadata tracking, and documentation.
• Work with business and analytics teams to resolve data quality issues.

Cloud & Modern Data Engineering Tools

• Gain hands-on experience with cloud data platforms such as AWS, Azure, or Google Cloud.
• Support automation workflows using orchestration tools such as Airflow, Prefect, Dagster, or similar.
• Learn and apply big data frameworks such as Spark, Kafka, Databricks, depending on project requirements.

Collaboration & Team Support

• Work closely with data scientists, analysts, software engineers, and business stakeholders.
• Participate in code reviews, sprint planning, and Agile ceremonies with mentorship from senior team members.
• Assist in documenting processes, data flows, and architectural components.

Upskilling & Learning Requirements

• Commit to ongoing learning in areas such as:
• Distributed data systems
• Cloud-native engineering
• CI/CD for data pipelines
• Data security and compliance
• Complete assigned training modules, certifications, or internal learning paths.

Required Qualifications

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Mathematics, or related field OR equivalent experience.
• 1–3 years of experience in data engineering, data analytics, software engineering, or related fields.
• Foundational knowledge of: SQL (intermediate level), Python or another scripting language, Data modeling concepts & ETL/ELT workflows.
• Understanding of relational databases (PostgreSQL, MySQL, SQL Server) and basic cloud concepts.
• Strong analytical, troubleshooting, and problem-solving skills.
• Eagerness to learn modern data engineering tools and cloud technologies.