Abacus
From Abacus's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
The role supports critical data operations in regulated environments and provides opportunities for hands-on learning and growth under the guidance of senior data engineers.
The Data Engineer is responsible for developing, maintaining, and supporting data pipelines using Python and SQL Server (T-SQL), while assisting with data integration from internal and external systems within a modern Azure-based data platform. The role supports critical data operations in regulated environments and provides opportunities for hands-on learning and growth under the guidance of senior data engineers.
Develop and maintain ETL pipelines using Python and SQL Server for data ingestion, transformation, and integration from various data sources, including structured files and RESTful APIs.
Write, test, and debug Python code for data processing, automation, and basic integrations following established standards and best practices.
Create and maintain T-SQL queries, views, and stored procedures to support business logic and reporting requirements.
Assist in building and operating data workflows using Azure Data Factory, Azure SQL, and Azure Blob Storage.
Support the monitoring of data pipelines and help troubleshoot data quality issues, pipeline failures, and performance problems.
Follow defined data quality, security, and compliance procedures in regulated environments such as healthcare and financial services.
Collaborate with data analysts, software engineers, and DevOps teams to understand data requirements and upstream systems.
Participate in code reviews, implement feedback, and continuously improve coding and engineering practices.
Create and maintain clear documentation for data pipelines, logic, and operational processes.
Bachelor’s Degree — Preferred
1–4 years of professional experience in Data Engineering, Software Engineering, or a related technical role — Required
Strong hands-on proficiency in Python, including writing functions, handling errors, and debugging code — Required
Experience working with relational databases, preferably SQL Server — Required
Strong working knowledge of SQL, including joins, aggregations, subqueries, and basic performance considerations — Required
Exposure to integrating or consuming data from RESTful APIs or external data sources — Required
Familiarity with Git or similar version control systems — Required
Strong analytical and problem-solving skills with the ability to learn quickly — Required
Experience with additional Azure services such as Azure Synapse, Azure DevOps, and Azure Functions
Understanding of data modeling and warehousing concepts
Exposure to cybersecurity data, SIEM tools, or SOC operations
Knowledge of .NET Framework and C#-based APIs, particularly in data consumption contexts
Background in MSP/MSSP environments or consulting
Familiarity with Power BI or other data visualization tools
Exposure to Azure data services such as Azure Data Factory, Azure SQL, or Blob Storage.
Basic understanding of ETL concepts, data validation, and pipeline monitoring.
Familiarity with data modeling fundamentals, including tables, keys, and relationships.
Awareness of data security, privacy, and compliance standards such as HIPAA and SOC2.
Experience with Power BI or other data visualization tools.
Basic understanding of application systems or APIs built using .NET or similar frameworks.
Ability to design and support scalable ETL pipelines using Python and SQL Server for data ingestion, transformation, and integration from diverse sources.
Ability to develop and optimize T-SQL stored procedures to support business logic and reporting needs.
Ability to support secure and efficient data workflows using Azure Data Factory, Azure SQL, and Azure Functions.
Ability to help ensure data quality, lineage, and compliance requirements are maintained.
Ability to collaborate with software engineering, data analytics, security, and DevOps teams.
Ability to monitor and troubleshoot pipeline failures and data discrepancies.
Ability to participate in code reviews and continuously improve engineering practices.
Ability to work effectively in a collaborative, fast-paced environment.
Good written and verbal communication skills.
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