Senior Developer Data Engineering

Quaker Houghton (US & CN); Conshohocken (Montgomery County); On-site; USD 120,000 - 180,000; Full time

Job summary

Quaker Houghton is seeking a Senior Developer – Data Engineering to design, develop, implement and operate data lakehouse solutions that power analytics and AI across the business.

The role combines hands-on technical work with strategy, driving data lakehouse architecture, data quality, security, and governance, while collaborating with IT and analytics teams to deliver scalable data platforms.

Qualifications

• Minimum 8 years of data engineering or equivalent experience.
• 5+ years of direct work experience in large-scale/enterprise technology projects.
• 5+ years hands-on experience across data engineering technologies (dbt, Snowflake, Iceberg, git, DevOps).
• Experience with cloud-native data engineering technologies (Snowflake, Databricks).
• Senior management experience with exposure to senior leadership and major initiatives.
• College degree preferably MIS or related field.

Responsibilities

• Actively participate in full life cycle of data lakehouse technology solutions.
• Evaluate, implement and administer data lakehouse technology, data flows, and governance standards; ensure data integrity.
• Provide hands-on day-to-day data engineering support and monitoring.
• Lead design, implementation and testing of data lakehouse technology.
• Collaborate with applications and analytics teams to support priorities and evolving needs.
• Manage small data engineering projects, on time, within scope and budget.
• Coordinate with cross-functional teams to minimize disruptions to business operations.
• Understand business data needs and ensure data engineering efforts meet them.
• Monitor performance and resolve issues; optimize for efficiency and latency.
• Document data flows, mappings, and transformation logic.

Skills

Data engineering

Large-scale projects

Hands-on data technologies

Senior leadership

Education

MIS or related computer science degree

Tools

dbt

Snowflake

Iceberg

git

Databricks

DevOps

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