Associate Director is responsible for leading, scaling, and optimizing the data engineering and operational functions of an organization. This role oversees the development and support of data pipelines & products, infrastructure, and automation to ensure reliable, secure, and efficient data delivery. The Associate Director collaborates with cross-functional teams to drive operational excellence, enhance incident response, and implement AI-enabled solutions for proactive issue resolution. This role requires deep expertise in data platforms, people leadership, process transformation, and the ability to communicate KPIs and strategic insights to senior leadership. The Associate Director is also accountable for developing high-performing teams, enabling data-driven decision-making, and ensuring enterprise data systems are aligned with evolving business priorities.
What you ll be doing:
- Lead and scale a global Data Engineering & Operations team, fostering a culture of accountability, innovation, and continuous improvement.
- Drive operational excellence by establishing best practices for data pipeline monitoring, automation, and incident management using AI/ML-enabled observability tools.
- Oversee data infrastructure and workflows , ensuring performance, scalability, security, and compliance across cloud platforms (AWS, GCP, Azure).
- Define and track KPIs for data availability, quality, reliability, and incident resolution; present key metrics and insights to senior leadership regularly.
- Enable automation-first culture by reducing repetitive incidents and streamlining issue resolution processes through smart alerting, auto-remediation, and orchestration enhancements.
- Collaborate with cross-functional teams (data engineers, analysts, product, platform, and governance teams) to align operational priorities with enterprise data strategy.
- Ensure effective SLA adherence , capacity planning, change management, and risk mitigation across all critical data services.
- Mentor and grow talent within the team, conducting regular performance reviews, skill development planning, and succession pipeline development.
- Stay current with trends in AI for IT operations (AIOps), SRE practices, and emerging technologies to future-proof data operations.
How You Will Succeed:
- Leadership & Vision : Inspire and guide a high-performing team by setting clear goals, fostering accountability, and creating a culture of continuous improvement.
- Operational Excellence : Establish reliable, scalable, and efficient operational processes for monitoring, alerting, and incident response focusing on SLAs, SLOs, and data uptime.
- AI-Enabled Optimization : Proactively identify repetitive incidents and leverage AIOps, machine learning, and automation to improve root cause analysis, reduce MTTR, and prevent recurrence.
- Cross-Functional Collaboration : Build strong partnerships with product, platform, and analytics teams to align operational priorities with business goals and data strategy.
- Strategic Communication : Translate operational KPIs, risk metrics, and platform health insights into executive-ready updates that support strategic decisions.
- Quality & Compliance Focus : Champion data quality, lifecycle management, and regulatory compliance (e.g., HIPAA, GDPR) within operational processes.
- Innovation & Scalability : Continuously evaluate tools, frameworks, and industry best practices to future-proof the data ecosystem and scale operations efficiently.
What You should Bring:
- Technical Expertise :
Proven knowledge of data pipelines, ETL/ELT frameworks, orchestration tools (e.g., Airflow, Control-M), cloud platforms (AWS, Azure, GCP), and big data technologies (Spark, Databricks, Redshift, etc.). - Operational Mindset :
Strong grasp of data SLAs/SLOs, incident response frameworks, observability tools (e.g., Datadog, Grafana), and performance tuning in large-scale environments. - AI & Automation Focus :
Understanding of AIOps or automation tools and the ability to identify opportunities to apply AI for root cause prediction, cost optimization, and reducing manual workload. - People & Process Skills :
Proven ability to lead diverse technical teams, mentor talent, and implement scalable processes for reliability, quality, and compliance. - Strategic Communication :
Ability to articulate complex operational metrics and trends to senior leadership in business-relevant language.
Basic Qualifications and Experience Requirement:
- Bachelor s or Master s degree in Computer Science, Information Systems, Data Engineering, or a related field.
- 12+ years of experience in data engineering, platform operations, or data infrastructure roles.
- Minimum 3 years of experience leading technical teams or managing global data operations.
- Hands-on expertise with data engineering tools (e.g., Spark, Databricks, Snowflake), orchestration platforms (e.g., Airflow, Control-M), and cloud services (AWS, Azure, or GCP).
- Strong understanding of data integration, data quality, observability, and monitoring at enterprise scale.
- Demonstrated experience in managing SLAs/SLOs, resolving production issues, and driving automation to reduce operational overhead.
- Exposure to AIOps or data operations automation practices is a strong plus.
- Strong communication skills and experience presenting metrics, KPIs, and strategic updates to senior leadership.
Additional Skills/Preferences:
- Domain experience in healthcare, pharmaceutical ( Customer Master, Product Master, Alignment Master, Activity, Consent etc. ), or regulated industries is a plus.
- Partner with and influence vendor resources on solution development to ensure understanding of data and technical direction for solutions as well as delivery
- AWS Certified Data Engineer - Associate
- Databricks Certified Data Engineer (Associate or Professional)
- AWS Certified Architect (Associate or Professional)
- Familiarity with AI/ML workflows and integrating machine learning models into data pipelines
