Data Engineer

Posted by ICONMA, LLC

Painted Post, NY

Permanent

Our Client, a Business Manufacturing and Suppply company, is looking for a Data Engineer for their Painted Post, NY location.

Responsibilities:

Embed within a cross-functional Agile team, participating in sprint planning, stand-ups, backlog refinement, and technical discussions.
Design, build, troubleshoot, and maintain ETL/ELT workflows that support application functionality, analytics, reporting, and scientific workflows.
Develop and manage data pipelines using Apache Airflow, ensuring reliable orchestration, scheduling, monitoring, and recovery of data processes.
Work with stakeholders including software developers, scientists, and engineers to understand data sources, workflow requirements, and downstream data needs.
Extract, transform, validate, and load data across systems, including relational databases such as Postgres SQL and Oracle.
Write, optimize, and maintain complex SQL queries, scripts, and transformation logic to support operational and analytical use cases.
Troubleshoot data quality issues, ETL failures, pipeline bottlenecks, and schema inconsistencies; identify root causes and implement durable solutions.
Support database exploration, data validation, and troubleshooting using tools such as DBeaver and related database utilities.
Evaluate and help adopt new data tools and technologies, including lightweight analytics and transformation solutions (e.g. DuckDB) where appropriate.
Collaborate with engineering teams to support reliable integration between data pipelines, applications, APIs, and downstream consumers.
Assist with schema evolution, data modeling, migration planning, and data consistency across systems.
Document pipeline logic, data dependencies, transformation rules, and operational procedures to support maintainability and team knowledge sharing.
Help improve data engineering standards, observability, testing practices, and operational reliability across the team.
Regularly interact with scientists and engineers to understand research and technical workflows; experience in scientific or research environments is a strong plus.

Requirements:

Experience designing, building, and troubleshooting ETL/ELT pipelines
Hands-on experience with workflow orchestration tools, preferably Apache Airflow
Strong experience writing and optimizing SQL
Experience working with relational databases, especially Postgres SQL and Oracle
Ability to develop and maintain data transformations, validation steps, and pipeline logic across multiple systems
Experience with database tools such as DBeaver or similar for query development, exploration, and troubleshooting
Familiarity with modern data processing and analytical tools such as DuckDB or interest in evaluating emerging data technologies
Understanding of data modeling, schema design, data integrity, and performance tuning
Experience troubleshooting pipeline failures, performance issues, and inconsistent or incomplete datasets
Familiarity with scripting or programming for pipeline development and automation; Python experience is strongly preferred
Understanding of version control and collaborative development workflows
Experience supporting production data systems with an emphasis on reliability, maintainability, and clear documentation
Confident collaborating with developers, scientists, analysts, and product stakeholders
Able to gather and clarify technical and data requirements and translate them into scalable data solutions
Strong communication skills around pipeline status, data quality issues, dependencies, and tradeoffs
Comfortable handling ambiguity, improving incomplete processes, and helping define best practices
Proactive in identifying opportunities to improve data workflows, tooling, performance, and operational stability
Strong analytical and problem-solving skills
High attention to detail and commitment to data quality, consistency, and reliability
Demonstrated initiative in troubleshooting issues and improving pipeline robustness
Curiosity and willingness to evaluate and adopt new tools, technologies, and approaches
Ability to balance immediate operational needs with long-term maintainability and scalability
Comfortable proposing improvements, collaborating across teams, and building trust through reliable execution