Senior Director - Client Data Solutions

About Cielo

Cielo is the world's leading Talent Acquisition Partner, creating careers for ambitious people by moving beyond traditional assumptions in talent acquisition. We deliver a better talent experience for everyone through Talent Acquisition, Search, Consulting, and Digital Accelerators™. Our fresh approach designs and builds comprehensive, proven solutions inspired by technology to find and retain unique talent that elevates our clients.

Job Description

The Senior Director - Client Data Solutions is a strategic leadership role responsible for defining and delivering the organization’s end-to-end client data ecosystem. This includes client integrations (ATS and external systems), data engineering, and client-facing reporting solutions. The role ensures seamless, scalable data flow from client environments into internal systems and productized data solutions, owning the design, delivery, and reliability of integrations that enable consistent, high-quality dashboards and reporting experiences. The Senior Director is accountable for templated, analytics-ready data products that power client reporting, while partnering with Business Intelligence and analytics teams.

As a senior member of the Client Technology leadership team, this role collaborates closely with Product Management, AI Engineering, and Client Services to align integration and data capabilities with product strategy, AI initiatives, and long-term platform evolution.

• Role: Remote - United States
• Work setup: Monday to Friday - FTE

Skills:

• Strong experience in Data engineering / Data platforms & integrations.
• Ability to identify the most efficient and stable ways to extract client data, ensure correct integration into internal systems, and translate into accurate reporting and dashboards.

Duties and Responsibilities:

• Team Leadership: Lead, mentor, and scale Data Engineering, Integration Engineering, and Client Reporting teams, providing technical guidance, career development, and performance management.
• Strategy & Execution: Define and execute the organization’s data integration and client data strategies, spanning integrations, data platform, and reporting enablement.
• Performance Measurement: Establish KPIs to measure data platform performance, reporting adoption, data quality, and business impact.
• Stakeholder Alignment: Partner with executive stakeholders across Product, AI Engineering, and Client Services to align data capabilities with business priorities.
• Governance & Architecture: Contribute to Engineering Leadership governance, including technical standards, architecture, and long-term strategy. Drive technical direction for data architecture, integration patterns, and platform scalability.
• Pipeline & Integration: Oversee the design and delivery of scalable, reliable data integration pipelines and workflows across client systems and internal platforms. Establish best practices for data engineering (testing, deployment, observability, documentation).
• Quality & Reliability: Guide implementation of data quality, monitoring, and reliability frameworks. Ensure compliance with data governance, security, and regulatory requirements.
• Technology Evolution: Continuously evaluate and evolve the data technology stack to support growth and innovation.
• Data Products: Own the strategy and delivery of client-facing data products, including dashboards, reporting layers, and embedded data experiences. Deliver scalable, templated reporting solutions.
• Data Modeling: Partner with Client Services and Operations to translate business needs into reusable, analytics-ready data models. Establish and maintain semantic layers, standardized metrics, and governed data definitions.
• Reporting Experience: Ensure performance, usability, and consistency of data powering all client reporting experiences.
• Product Integration: Collaborate with Product Management to embed reporting capabilities into core platform offerings and align data capabilities with product roadmaps.
• AI/ML Support: Collaborate with AI Engineering to ensure enterprise data supports AI/ML model development, deployment, and consumption.
• Client Needs: Work closely with client-facing teams to understand customer needs and deliver scalable, repeatable data solutions.
• Communication: Act as a bridge between technical and business stakeholders to drive alignment and clarity on data initiatives.
• Culture: Champion a data-driven culture across the organization.
• Operations: Oversee planning, prioritization, and execution of data integration and platform initiatives. Manage budgets, vendor relationships, and technology investments. Establish processes for monitoring, troubleshooting, and maintaining production data systems.
• Efficiency: Drive continuous improvement in scalability, reliability, and efficiency of data operations. Enable faster delivery of reporting capabilities through standardization and reusable data assets.

Supervisory Responsibilities:

Assist with daily activities, provide guidance, conduct performance reviews, and support the professional growth of the Data Engineering, Integration Engineering, and Client Reporting leaders.

Qualifications:

• Education: Bachelor’s degree in Computer Science, Information Systems, Data Science, or a related field strongly preferred.
• Experience:
• 8+ years of experience in data engineering, data platforms, or integration roles.
• 5+ years of experience leading technical teams, including managing managers.
• Deep experience with modern data architecture, ETL/ELT, and integration frameworks.
• Proven track record of delivering client-facing data solutions or data products at scale.
• Demonstrated ability to align technical solutions with business outcomes.
• Strong understanding of core tech stack: AWS, Microsoft Azure, Apache Airflow, Docker, Python (Pandas), RDBMS (Snowflake, PostgreSQL), and NoSQL (Redis, DynamoDB).
• Extensive experience with data modeling, ETL/ELT processes, and database technologies (Snowflake, PostgreSQL, NoSQL).
• Experience with API design, development, and integration patterns.
• Experience working with AI/ML teams to support data requirements.
• Track record of successfully delivering complex data integration projects.
• Experience in talent acquisition technology, HR systems, or related industries preferred.
• Exposure:
• Knowledge of data governance, security, and compliance requirements.
• Proficiency in data quality management and monitoring tools.
• Understanding of the full project/program execution lifecycle.
• Expert knowledge in various workstreams (change management, risk assessment, compliance, etc.).
• Commercially astute.
• Proficient in Word, Excel, PowerPoint, Outlook, Smartsheet, Lucidchart, and related productivity software.