Director - Data Engineering & AI Solutions

Director Data Engineering & AI Solutions

Job Description :

Strategic Leadership & Delivery :

- Provide strategic direction and oversight for the Data Engineering and AI Solutions practice, driving business growth and technological innovation.

- We need an experienced candidate from a strong IT services company background who has led large-scale data engineering initiatives across diverse technological stacks.

- Establish the technical vision and strategy for enterprise data platforms leveraging both enterprise tools (Informatica, Talend, Snowflake) and open-source frameworks (Apache Spark, Kafka, Airflow, Hadoop).

- Define organizational standards for data architecture, ETL/ELT processes, and data governance to support data-intensive applications and AI solution integration.

- Develop and implement cost optimization frameworks for large-scale data platforms, ensuring efficient resource utilization without compromising performance.

- Lead performance optimization initiatives to enhance data processing efficiency, reduce latency, and improve throughput for mission-critical data pipelines.

- Direct cross-functional collaboration between Sales, Delivery, Data Science, and Solution Architecture teams to align on enterprise-grade data platform strategies that enable advanced analytics and AI workloads.

- Establish center of excellence for data engineering best practices, ensuring high performance, reliability, and maintainability of data pipelines across the organization.

- Oversee multiple concurrent implementations of complex data infrastructure, including data lakes, data warehouses, streaming solutions, and real-time processing capabilities.

- Act as an executive-level advisor to client leadership on data engineering strategy, helping them modernize their data infrastructure to support AI and advanced analytics use cases.

- Build, manage, and scale multiple teams of data engineers, DevOps specialists, and cloud architects across different regions and projects.

- Develop data platform KPIs, establish performance metrics, and implement data governance frameworks at an organizational level.

Business Development & Go-to-Market Strategy :

- Engage with C-level executives at prospective clients to understand their strategic data engineering challenges and articulate our value proposition.

- Develop and execute the go-to-market strategy for data engineering and AI solutions practice, identifying key industries and target accounts.

- Direct the development of thought leadership content, service offerings, and IP assets in the data engineering and AI space.

- Review and approve complex solution architectures demonstrating how our data engineering expertise can prepare organizations for AI adoption.

- Oversee the creation of compelling proposals and presentations showcasing modern data engineering approaches using both enterprise tools and open source frameworks.

- Partner with sales leadership to establish revenue targets, develop account strategies, and drive significant deals to closure.

- Lead high-stakes client meetings and executive briefings to position the company as a leader in data engineering and AI-ready infrastructure.

- Spearhead the company's growth by identifying and pursuing new business opportunities in target markets (US & Europe), with P&L responsibility for the practice.

Executive Client Relationship Management :

- Establish and nurture executive-level relationships with CTOs, CDOs, CIOs, and business leaders responsible for data strategy within client organizations.

- Own overall client satisfaction metrics for the data engineering practice, ensuring delivery of robust, scalable solutions that enable advanced analytics and AI capabilities.

- Serve as the escalation point for strategic accounts and develop long-term account plans to expand relationships and increase wallet share.

- Facilitate executive roundtables, industry forums, and thought leadership events to position the company as an industry leader in data engineering.

Industry Domain Expertise :

- Demonstrate deep understanding of data engineering challenges and opportunities specific to one or more of the following industries: Retail, Telecommunications, BFSI (Banking, Financial Services, and Insurance), or Healthcare.

- Leverage industry-specific knowledge to design data platforms that address domain-specific requirements, such as:

- Retail: Customer 360, supply chain optimization, inventory management, personalization engines, and omnichannel analytics

- Telecommunications: Network performance analytics, customer churn prediction, service optimization, and regulatory compliance

- BFSI: Fraud detection, risk modeling, regulatory reporting, customer segmentation, and real-time transaction processing

- Healthcare: Clinical analytics, patient journey optimization, claims processing, regulatory compliance (HIPAA), and healthcare interoperability standards

- Apply domain knowledge to configure data pipelines that meet industry-specific regulatory and compliance requirements.

- Translate industry-specific business requirements into appropriate data engineering architectures and solutions.

- Stay current with industry trends and emerging use cases for data engineering and AI in chosen verticals.

- Build relationships with industry partners and technology vendors specializing in domain-specific data solutions.

Qualifications & Experience :

- Bachelor's or master's degree in Computer Science, Data Engineering, or related field; MBA or advanced degree preferred.

- 15+ years of experience in data engineering, distributed systems, or cloud data platforms, with at least 5 years in a director-level or equivalent leadership position.

- Proven track record of leading successful data engineering practices or departments in a services organization serving US and European markets.

- Strategic vision for designing and implementing enterprise data architectures that integrate with modern AI frameworks at scale.

- Technical background with major enterprise data tools (Informatica, IBM DataStage, Talend, Snowflake, Databricks) and open source frameworks (Hadoop, Spark, Kafka, Airflow).

- Proven experience with Python-based data engineering ecosystems and frameworks (pandas, NumPy, PySpark, Dask).

- Strong understanding of production-grade data pipeline development, orchestration, and monitoring with tools like Apache - Airflow, Luigi, or Prefect.

- Experience overseeing the development of data pipelines and platforms that integrate with AI/ML workflows and models.

- Track record of designing and implementing both batch and real-time data pipelines to support enterprise-scale data processing requirements.

- Demonstrated success in data platform cost optimization strategies, including resource rightsizing, query optimization, and efficient data storage implementations.

- Experience implementing performance tuning for large-scale data pipelines, including parallelization strategies, memory management, and query optimization.

- Proven ability to reduce cloud infrastructure costs while maintaining or improving data processing performance.

- Demonstrated ability to lead CXO-level technical discussions about enterprise data strategy and digital transformation initiatives.

- Understanding of technical architecture principles and patterns for enterprise data platforms.

- Experience establishing cloud platform strategies with AWS, Azure, or GCP for large-scale data services.

- Track record of implementing organizational data governance, data quality, and data cataloging solutions.

- Strong understanding of emerging data engineering technologies and ability to develop innovation roadmaps.

- Executive-level communication, presentation, and interpersonal skills, with the ability to influence C-suite stakeholders and drive strategic partnerships.

- P&L management experience with demonstrated success growing revenue and managing departmental budgets.

- Ability to drive organizational change and work in a fast-paced, dynamic environment.

- Extensive experience working with US and European enterprise clients is required.

- History of successful business development and account expansion in professional services organizations.

Desirable Skills :

- Strategic understanding of AI/ML integration with enterprise data platforms and operationalizing ML workflows at scale.

- Experience developing service offerings around real-time data processing and streaming architectures.

- Strategic perspective on containerization (Docker) and orchestration (Kubernetes) for enterprise data workloads.

- Leadership in establishing DataOps and MLOps practices at an organizational level.

- Experience navigating regulatory frameworks related to data privacy and compliance in the US and Europe (GDPR, CCPA).

- Experience with merger integration or practice acquisition in professional services.

- International work experience in US and European markets.