Build the Future of Smarter Demand

Role Overview

We are seeking an experienced Director, Sales to lead and grow strategic client relationships across India. This role is responsible for delivering measurable revenue growth, client retention and campaign success within the larger B2B mar-tech market.

Key Responsibilities

Client Leadership

• Own a portfolio of strategic enterprise and mid-market clients across India.
• Build strong relationships with marketing leaders, demand generation teams, sales leaders and agency partners.
• Understand each client’s business objectives, target markets, pipeline goals and campaign requirements.
• Lead regular business reviews and provide clear recommendations based on campaign performance.
• Maintain high client satisfaction, retention and renewal rates.

Revenue Growth

• Achieve quarterly and annual revenue targets across new and existing accounts.
• Identify opportunities to expand relationships through cross-selling and upselling.
• Develop account plans for high-value clients and prospects.
• Manage the full commercial process, including proposals, pricing, negotiations and contracts.
• Maintain accurate revenue forecasts and opportunity information in the CRM.

Campaign Strategy and Delivery

• Translate client objectives into effective mar-tech programs.
• Work closely with campaign operations, customer success and data teams to ensure successful delivery.
• Monitor lead quality, pacing, volume and performance against agreed KPIs.
• Resolve delivery or quality issues quickly and communicate proactively with clients.

Experience and Qualifications

• Five or more years of experience in B2B media, demand generation, advertising technology or marketing services.
• Demonstrated success managing and growing enterprise client accounts.
• Established relationships within the India technology, media or agency ecosystem are highly desirable.
• Proven ability to meet revenue targets and manage complex commercial negotiations.
• Experience working with global delivery teams and cross-functional stakeholders.
• Familiarity with CRM and campaign reporting platforms.

Skills and Attributes

• Commercially focused and consultative in approach.
• Excellent relationship management, presentation and negotiation skills.
• Strong analytical ability and confidence interpreting campaign performance data.
• Proactive, accountable and capable of managing multiple priorities.
• Collaborative and effective in a fast-paced, geographically distributed business.
• Excellent written and verbal communication skills.

Key Responsibilities

Strategy

• Support the data engineering team in implementing data platform initiatives across enterprise data environments.
• Assist in migration tasks as part of modernising data warehouses, data lakes, lakehouses and cloud data platforms.
• Follow established standards for data ingestion, transformation, orchestration, modelling, storage and consumption.
• Flag opportunities for improving pipeline performance and raise them with senior engineers.

Business

• Work with senior engineers and analysts to understand data requirements for assigned projects.
• Contribute to the build and delivery of data products supporting analytics, reporting and operational use cases.
• Participate in prioritisation discussions and provide input based on task complexity and effort.

Processes

• Build and maintain ETL/ELT pipelines under the guidance of senior team members.
• Follow engineering standards for data ingestion, transformation, orchestration, testing and deployment.
• Support automation efforts to reduce manual intervention in day-to-day data processes.
• Write and maintain automated tests; support CI/CD pipeline usage and basic monitoring setup.
• Ensure pipelines you build are clean, documented and maintainable.
• Assist with production support, including basic troubleshooting and escalation of incidents.

Data Quality

• Apply data quality checks and validation rules to pipelines you own.
• Help implement automated validation, reconciliation and monitoring processes.
• Track data quality metrics (availability, completeness, accuracy) for assigned datasets.
• Support investigation of data quality issues, escalating systemic issues to senior team members.

Governance & Security

• Follow enterprise data governance, security, privacy and regulatory guidelines in all engineering work.
• Apply access controls, data classification and retention rules as defined by the platform team.
• Maintain accurate data lineage, technical metadata and source-to-target documentation for your work.
• Support audits and reviews by providing documentation and technical detail as requested.

Learning & Collaboration

• Learn from and collaborate closely with senior engineers, actively seeking feedback to improve technical skills.
• Follow established coding standards, development practices and engineering best practices.
• Stay current with modern cloud, data engineering and analytics tools relevant to the team's stack.
• Manage your own task list and communicate progress, blockers and timelines clearly to your lead.

Key Responsibilities

Strategy

• Define and execute data engineering and platform strategy across enterprise data environments.
• Lead the modernisation of data architecture, data warehouses, data lakes, lakehouses and cloud data platforms.
• Drive migration from legacy data environments to scalable and cost-effective modern data platforms.
• Establish standards for data ingestion, transformation, orchestration, modelling, storage and consumption.
• Identify opportunities to improve platform scalability, performance, reliability and automation.

Business

• Partner with business and technology leaders to understand data requirements and translate them into scalable technical solutions.
• Lead delivery of enterprise data products and platforms supporting analytics, reporting and operational use cases.
• Work with stakeholders to prioritise data engineering initiatives based on business value and strategic importance.
• Provide technical guidance to senior leadership on data architecture, platform capabilities and technology investments.

Processes

• Lead the development and optimisation of complex ETL/ELT pipelines and data processing frameworks.
• Establish engineering standards for data ingestion, transformation, orchestration, testing and deployment.
• Drive automation across data engineering processes to improve reliability and reduce manual intervention.
• Implement CI/CD, automated testing, monitoring and observability for critical data pipelines.
• Ensure data pipelines are scalable, resilient, secure and maintainable.
• Oversee production support, incident management and root-cause analysis for critical data platforms.

Data Quality

• Establish data quality frameworks across critical enterprise datasets and pipelines.
• Implement automated validation, reconciliation, monitoring and exception management processes.
• Define SLAs and KPIs for data availability, completeness, accuracy and pipeline performance.
• Partner with data owners and business teams to resolve systemic data quality issues.

Governance & Security

• Ensure data platforms comply with enterprise data governance, security, privacy and regulatory requirements.
• Implement appropriate access controls, data classification, retention and audit mechanisms.
• Maintain data lineage, technical metadata and source-to-target documentation.
• Support audits, risk assessments and regulatory reviews relating to data platforms.

Leadership

• Lead and mentor data engineering and platform teams.
• Establish technical standards, development practices and engineering best practices.
• Manage vendors, technology partners and third-party data providers where required.
• Drive technical innovation and adoption of modern cloud, data engineering and analytics technologies.
• Manage multiple strategic initiatives while ensuring delivery against agreed timelines, budgets and quality standards.

Qualifications

Education & Experience

• Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related field.
• 12–18+ years of overall experience in data engineering, data platforms, or data architecture, including at least 5 years in a leadership or managerial capacity.
• Demonstrated experience leading large-scale data platform modernisation or cloud migration initiatives.
• Proven track record of building and scaling data engineering teams in a matrixed, enterprise environment.

Preferred / Nice to Have

• Relevant certifications (e.g., AWS/Azure/GCP data or solutions architect certifications, Databricks certifications, Snowflake SnowPro).
• Experience in a regulated industry (BFSI, healthcare, or similar) where data governance and compliance are business-critical.
• Exposure to AI/ML platform enablement or MLOps as it pertains to data infrastructure.

Key Responsibilities

Strategy

• Define and execute data engineering and platform strategy across enterprise data environments.
• Lead the modernisation of data architecture, data warehouses, data lakes, lakehouses and cloud data platforms.
• Drive migration from legacy data environments to scalable and cost-effective modern data platforms.
• Establish standards for data ingestion, transformation, orchestration, modelling, storage and consumption.
• Identify opportunities to improve platform scalability, performance, reliability and automation.

Business

• Partner with business and technology leaders to understand data requirements and translate them into scalable technical solutions.
• Lead delivery of enterprise data products and platforms supporting analytics, reporting and operational use cases.
• Work with stakeholders to prioritise data engineering initiatives based on business value and strategic importance.
• Provide technical guidance to senior leadership on data architecture, platform capabilities and technology investments.

Processes

• Lead the development and optimisation of complex ETL/ELT pipelines and data processing frameworks.
• Establish engineering standards for data ingestion, transformation, orchestration, testing and deployment.
• Drive automation across data engineering processes to improve reliability and reduce manual intervention.
• Implement CI/CD, automated testing, monitoring and observability for critical data pipelines.
• Ensure data pipelines are scalable, resilient, secure and maintainable.
• Oversee production support, incident management and root-cause analysis for critical data platforms.

Data Quality

• Establish data quality frameworks across critical enterprise datasets and pipelines.
• Implement automated validation, reconciliation, monitoring and exception management processes.
• Define SLAs and KPIs for data availability, completeness, accuracy and pipeline performance.
• Partner with data owners and business teams to resolve systemic data quality issues.

Governance & Security

• Ensure data platforms comply with enterprise data governance, security, privacy and regulatory requirements.
• Implement appropriate access controls, data classification, retention and audit mechanisms.
• Maintain data lineage, technical metadata and source-to-target documentation.
• Support audits, risk assessments and regulatory reviews relating to data platforms.

Leadership

• Lead and mentor data engineering and platform teams.
• Establish technical standards, development practices and engineering best practices.
• Manage vendors, technology partners and third-party data providers where required.
• Drive technical innovation and adoption of modern cloud, data engineering and analytics technologies.
• Manage multiple strategic initiatives while ensuring delivery against agreed timelines, budgets and quality standards.

Qualifications

Education & Experience

• Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related field.
• 8–10+ years of overall experience in data engineering, data platforms, or data architecture, including at least 5 years in a leadership or managerial capacity.
• Demonstrated experience leading large-scale data platform modernisation or cloud migration initiatives.
• Proven track record of building and scaling data engineering teams in a matrixed, enterprise environment.

Preferred / Nice to Have

• Relevant certifications (e.g., AWS/Azure/GCP data or solutions architect certifications, Databricks certifications, Snowflake SnowPro).
• Experience in a regulated industry (BFSI, healthcare, or similar) where data governance and compliance are business-critical.
• Exposure to AI/ML platform enablement or MLOps as it pertains to data infrastructure.