Senior Developer Data Engineering

Quaker Houghton; Pennsylvania; On-site; USD 130,000 - 180,000; Full time

Job summary

Quaker Houghton is seeking a Senior Developer – Data Engineering to lead hands-on design, development, implementation and operations of data platforms. The role supports data analytics and AI initiatives across the organization and will drive robust, scalable data lakehouse architectures.

Reporting to the Manager – Data Architecture & Engineering, you will collaborate with IT and analytics teams, lead data engineering projects, enforce governance, and ensure data quality, security and compliance

Qualifications

• 8+ years of data engineering or equivalent experience preferred.
• 5+ years of direct work on large scale enterprise technology projects and modern warehouse architectures.
• 5+ years hands-on experience across multiple data engineering technologies (dbt, Snowflake, Iceberg, Git, DevOps).
• Experience in cloud-native data engineering technologies (Snowflake, Databricks).
• Bachelor's degree preferred, MIS or related field.

Responsibilities

• Participate in full life cycle of data lakehouse technology solutions.
• Evaluate, implement and administer data lakehouse systems and data flows per governance standards.
• Provide hands-on day-to-day support and monitoring of data engineering tools.
• Lead design, implementation and testing of data lakehouse technology.
• Collaborate with applications and analytics teams to support portfolio priorities.
• Manage small data engineering projects delivering on time and within budget.
• Coordinate with cross-functional teams to minimize disruptions to operations.
• Work with stakeholders to understand data needs and ensure alignment.
• Monitor performance of data pipelines and optimize latency.
• Implement data security measures within engineering processes.
• Document data engineering processes, flows, mappings, and transformation logic.
• Provide regular status reports to senior management.

Skills

Data lakehouse

Snowflake

dbt

Iceberg

Git

DevOps

Cloud native data engineering

Senior leadership exposure

Education

MIS or computer/information services degree

Tools

dbt

Snowflake

Iceberg

Git

Databricks

Job description

Reporting to the Manager – Data Architecture & Engineering, the Senior Developer – Data Engineering at Quaker Houghton will hands on in technology design, development, implementation and operational activities required to support Quaker Houghton business operations and long-term strategy. This role will actively participate the company wide effort to normalize, standardize and build operational processes for all data that is used in data analytics and artificial intelligence within the Quaker Houghton technology landscape. Bringing hands‑on technical expertise in data lakes, modern data warehousing, and data lakehouse architectures, and a deep understanding of data architecture and engineering this role will execute the data analytics strategy to ensure that data is consistently and accurately processed, enabling seamless data flow through a data lakehouse architecture, as well as the implementation of supporting technology, ensuring they have the required skills and knowledge to both implement and support the selected tool/s, whilst maintaining and gracefully retiring the existing analytic solutions. Working closely with the other Digital Information Technology teams, the role must ensure the robustness of the solution, including redundancy, performance monitoring, data quality, compliance, security and completeness of the data it handles. This role is critical in supporting the organization’s data-driven initiatives by ensuring that certified data is available, reliable, and secure.

Accountabilities

List the primary job responsibilities and activities in order of importance.

• Actively participate in the full life cycle of data lakehouse technology solutions aligned with the overall business

and technology goals and objectives.

• Participate in the evaluation, implementation and administration of data lakehouse technology, systems,

processes, data flows, and dependencies per architectural and data governance standards; ensures data integrity across the company systems.

Provides hands-on day-to-day support and monitoring of data engineering, tools and process

• Play a hands‑on role in projects in the design, implementation and testing of data lakehouse technology
• Partner with applications and analytics teams to support agreed portfolio priorities and continue to develop and enhance the integration technologies to evolving technology and business needs.

• Manage small data engineering projects, ensuring they are delivered on time, within scope, and within budget.

• Coordinate with cross-functional teams to ensure seamless integration and minimize disruptions to business operations.

• Work closely with business stakeholders to understand their data needs and ensure that data engineering efforts meet those needs.

• Monitor the performance of data engineering processes and systems, identifying and resolving issues proactively.

• Optimize data engineering to improve efficiency and reduce latency.

• Implement data security measures within the engineering processes to protect sensitive information continuously assessing and mitigating risks associated with data engineering activities.

• Document data engineering processes, including data flows, mappings, and transformation logic.

• Provide regular reports on the status, performance, and outcomes of data engineering initiatives to senior management

Work Environment / Ehs Responsibilities

• Sedentary category requiring exertion up to 10 lbs. of force occasionally and uses negligible amounts of force to move objects. Sedentary work involves sitting most of the time.
• Occasional: Standing: Remaining on one's feet without moving.
• Frequent: Sitting: Remaining in seated position
• Frequent: Reaching below shoulders: Extending arms in any direction below shoulders.
• Continuous: Communication: Expressing or exchanging written/verbal/electronic information.

Continuous: Vision: Clarity of near vision at 20 inches or less and far vision at 20 feet or more with depth perception, peripheral vision, color vision.

Education, Experience and Skills/Competencies To Perform The Job Duties

Minimum of 8 years of data engineering or equivalent experience is preferred.

• 5+ years of direct work experience in large scale/enterprise technology projects, with at least five years of direct experience relating to creation and execution of modern warehouse architectures
• 5+ years of hands‑on experience across multiple data engineering technologies, (e.g. dbt, Snowflake, Iceberg, git, DevOps)
• Experience working in cloud native data engineering technologies (e.g. Snowflake, Databricks)
• Experience in a senior management role, with exposure to senior leadership and accountable for major initiatives within the corporation or for business clients.
• College degree required, preferably in MIS or other computer/information services discipline