Senior Data Engineer

Heidrick & Struggles•Chicago, IL

11d

About The Position

Heidrick & Struggles is a premier provider of global leadership advisory and on-demand talent solutions, serving the senior-level talent and consulting needs of the world's top organizations. In our role as trusted leadership advisors, we partner with our clients to develop future-ready leaders and organizations, bringing together our services and offerings in executive search leadership assessment and development, organization and team acceleration, culture shaping and on-demand, independent talent solutions. Heidrick & Struggles pioneered the profession of executive search more than 70 years ago. Today, the firm provides integrated talent and human capital solutions to help our clients change the world, one leadership team at a time.® Additional information on the firm can be found at www.heidrick.com . The Role We are seeking a highly skilled, hands‑on Senior Data Engineer with 4–5 years of experience to help shape and scale our data foundation in support of Heidrick & Struggles’ mission to transform executive search and leadership advisory through data‑driven insights. As a senior member of the data engineering team, you will own complex data solutions, drive best practices, and mentor junior engineers while ensuring our data platform is scalable, governed, and reliable. You bring a builder’s mindset and strong sense of ownership, connecting deeply to business needs to design robust pipelines, standardize structures, optimize performance, and enable trusted, self‑service analytics and AI across the firm. This role sits at the intersection of engineering and business strategy, requiring close collaboration with data science, analytics, product, and business stakeholders.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Information Technology, Data & Analytics, Data Science, or a related field, or equivalent practical experience.
4–5 years of experience building and maintaining data lake/warehouse solutions and production‑grade data pipelines.
Expert‑level proficiency in SQL, including complex queries, optimization, and stored procedures for data processing (expected “10/10” SQL capability).
Hands‑on experience with Snowflake (data modeling, performance tuning, and cost management).
Proficiency in Python for data processing, orchestration, and automation.
Experience designing, implementing, and operating ETL/ELT pipelines using modern pipeline tools and orchestration frameworks.
Demonstrated experience mentoring or guiding junior engineers and contributing to team‑level technical decisions.
Solid understanding of data warehousing concepts, data modeling, and best practices for structured and semi‑structured data.
Familiarity with CI/CD practices and tools (e.g., Git‑based workflows, automated testing, deployment pipelines).
Excellent written and verbal communication skills, with the ability to explain complex technical concepts to non‑technical stakeholders.

Nice To Haves

Experience with modern data stack tools such as FiveTran, or similar technologies.
Exposure to data governance, data quality frameworks, and master or reference data management initiatives.
Experience working in agile environments and leading workstreams or key stories within a cross‑functional team

Responsibilities

Design, develop, and maintain scalable, secure data pipelines, data models, and warehouse/lake environments, primarily on Snowflake.
Own end‑to‑end data solutions from requirements gathering and design through implementation, testing, deployment, and iteration.
Partner closely with cross‑functional stakeholders to translate business problems into robust data solutions, ensuring data is accessible, trustworthy, and actionable.
Promote and contribute to best practices in data engineering (coding standards, testing, observability, documentation, and CI/CD).
Mentor and support junior data engineers through code reviews, pairing, and guidance on design and implementation.
Perform performance tuning, cost optimization, and reliability improvements in Snowflake and associated data pipelines.
Contribute to documentation of data models, dictionaries, data flow maps, and metadata to support transparency and data governance.
Collaborate with technical teams and stakeholders to implement data capture, ETL/ELT, validation, and QA, including automated quality checks and monitoring.
Support the development and enforcement of data governance, access controls, and security policies in partnership with data governance and security teams.
Stay up to date on emerging technologies, tools, and best practices in data engineering and bring forward recommendations that evolve our data platform.