Data lead

1. Group Data Infrastructure & Integration

• Audit the current state of data systems across all entities — ERPs, CRMs, LMS platforms, admissions systems, marketing tools, accounting software — and produce a consolidated data landscape.
• Design and implement a centralised data warehouse or lakehouse architecture that aggregates data from all entity systems into a single, queryable layer accessible to the Chairman's Office.
• Build and maintain ETL/ELT pipelines from source systems (likely a mix of SAP, Tally, Mastersoft , Salesforce, Merritto, custom LMS, Google Ads, Meta Ads, and institution-specific tools) into the central data layer.
• Ensure data pipelines are reliable, monitored, and automatically alerting on failures — the Chairman's Office must be able to trust the numbers.
• Evaluate and implement appropriate data stack tooling (e.g., dbt, Airbyte, BigQuery/Snowflake, Metabase/Superset/Power BI) proportionate to group scale and IT capability.

2. Reporting Dashboards & EP Office Intelligence

• Build and maintain the group's master performance dashboard — a single-pane view of KPIs across the Education Trust and MIT Group Ventures, updated automatically on a daily or weekly basis.
• Design entity-level dashboards for Management — so operating leaders have real-time visibility into their own performance before the monthly Chairman review.
• Own the data layer underlying the monthly review packs prepared by the Senior Strategic Analyst — ensure data is pre-validated, reconciled, and ready for narrative synthesis.
• Build early-warning alerts: automated flags when enrolment targets, collection rates, marketing spend efficiency, or outlet revenue deviate materially from plan.
• Produce a monthly data health report for the EP— flagging data quality issues, missing data, and system gaps across entities.

3. AI Data Partnership — Working Alongside the AI Lead

• Work in close partnership with the EP Office AI Lead to ensure all AI applications and platforms deployed across MIT Group businesses are fed accurate, clean, and well-structured data.
• Serve as the data layer owner for every AI initiative across the group
• Collaborate with the AI Lead on data requirements for each AI use case: define what data is needed, in what format, at what frequency, and build the pipelines to supply it reliably.
• Own data quality validation for AI inputs — ensuring that models and AI applications do not produce misleading outputs due to dirty, incomplete, or mis-labelled source data from group systems.
• Jointly review AI application performance with the AI Lead on a monthly basis — identifying where data gaps, drift, or schema changes may be degrading model or application quality.
• Build and maintain a group-level AI data catalogue: a documented inventory of all datasets currently feeding AI applications, their update frequency, ownership, and quality status.

4. Data Governance & Quality

• Define and enforce a group-wide data dictionary — standard definitions for all key metrics (e.g., 'confirmed enrolment', 'net revenue', 'active student') so that the same term means the same thing across all 7+ institutions.
• Establish data ownership accountability at each entity — designate data stewards and put in place lightweight governance processes for data entry quality at source.
• Implement data validation checks within pipelines to catch errors, duplicates, and outliers before they reach Chairman's Office dashboards.
• Ensure all data handling complies with applicable data protection regulations (IT Act, DPDPA 2023) and institutional data policies.
• Maintain a data audit trail for all EP Office reporting — so any number in a board deck can be traced back to its source within minutes.

5. Analytical Modelling & Decision Support

• Build and maintain financial and operational models that underpin strategic decisions — enrolment forecasting, revenue projections, campus capacity modelling, marketing attribution.
• Develop cohort analyses and longitudinal student data models to support academic performance tracking, attrition analysis, and placement outcome reporting.
• Build scenario models for significant decisions: new campus feasibility, programme mix optimisation, pricing sensitivity, marketing budget allocation.
• Produce ad hoc analytical outputs as directed by the Chairman — turning a strategic question into a data-backed answer within 48-72 hours for standard requests.

6. Data Capability Building Across Entities

• Work with functional heads (Finance, Admissions, Marketing, HR, Placement) across entities to improve the quality and consistency of data captured at source.
• Train entity-level analysts and reporting staff on standard tools and data definitions — so the Chairman's Office is not the only node with data literacy.
• Evaluate and recommend BI or analytics tools for entity-level use — enabling operating leaders to self-serve on routine reporting rather than escalating every data request.
• Document all data architecture, pipeline logic, and dashboard definitions — ensuring institutional knowledge does not reside in one person.

Experience

• 7–10 years of total experience, with at least 4 years in a data engineering, analytics engineering, or senior data analyst role.
• Backgrounds that work well: Senior Data Analyst or Analytics Engineer at a mid-to-large ed-tech, consumer, or financial services company; Data Lead or Head of Data at a growth-stage startup with multi-source data complexity; BI Lead or Data Platform engineer at a professional services or institutional group.
• Prior experience in education, multi-campus institutions, or multi-unit consumer businesses (F&B, retail) is a strong plus.
• Has built or owned a data warehouse or centralised analytics platform from scratch — not just consumed one built by others.

Technical Skills

• SQL: expert-level, comfortable with complex joins, window functions, CTEs, and query optimisation across large datasets.
• Python: proficient for data pipeline scripting, transformation logic, and exploratory analysis (Pandas, NumPy).
• Data pipeline & orchestration: hands-on experience with at least one modern ETL/ELT tool (Airbyte, Fivetran, dbt, Apache Airflow, or equivalent).
• Cloud data warehouse: working experience with BigQuery, Snowflake, Redshift, or Azure Synapse.
• BI & visualisation: proficient in at least one BI tool — Power BI strongly preferred; Metabase, Superset, or Looker also acceptable.
• Data modelling: understanding of dimensional modelling, star/snowflake schemas, and data vault concepts.
• Familiarity with common source systems: Tally/SAP for finance, Salesforce or equivalent CRM, Google Ads/Meta Ads APIs, LMS platforms.