Biological E. Limited

Lead Data Engineering : Enterprise Data, Analytics, Cloud Platforms & AI

Location : Jubilee Hills, Hyderabad (Work from Office)

Experience : 1218+ years overall, with significant experience leading enterprise data strategy, architecture, modern cloud data platforms, data governance and large-scale transformation programmes

Reports to : Group Head – AI & Technology

Role Summary:

We are looking for a Head Data Engineering who will own the complete data strategy for a large enterprise organisation, from a business-aligned data vision and operating model through enterprise architecture, platform modernisation, governance, delivery oversight and measurable business outcomes.

The role will define and govern the enterprise data strategy, target-state architecture, technology roadmap and investment priorities across data engineering, data warehousing and lakehouse, integration, analytics, BI, data governance, data quality, security, metadata, AI and data products. You will translate business priorities into a pragmatic multi-year data roadmap and ensure that programmes and platforms stay aligned to the enterprise architecture and the value agenda.

The role requires solid executive stakeholder management, architecture leadership and the ability to lead large multidisciplinary teams and partners. Deep hands-on technical knowledge is expected, so that you can drive architecture decisions, standards and technical governance. The primary accountability, though, is enterprise strategy, outcomes, operating model and delivery leadership.

Key Responsibilities

1. Enterprise Data Strategy & Vision

- Own and continuously evolve the enterprise data strategy, aligned to business strategy, operating priorities and transformation objectives.
- Define the enterprise data vision, principles, capabilities, target operating model and multi-year transformation roadmap.
- Identify high-value data opportunities and prioritise data products, analytics, modernisation and AI initiatives on business value, feasibility and risk.
- Establish investment priorities, business cases, success measures and executive-level reporting for the data portfolio.
- Drive adoption of data as an enterprise asset, with clear accountability for data ownership and outcomes.

2. Enterprise Data Architecture & Platform Strategy

- Define current-state and target-state enterprise data architecture covering sources, ingestion, integration, storage, processing, serving, analytics, BI and AI.
- Own the architecture strategy for cloud data platforms, including Snowflake, AWS, Azure / Microsoft Fabric and hybrid enterprise environments.
- Define platform selection principles, reference architectures, technology standards and architecture guardrails.
- Lead modernisation of legacy data warehouses, ETL estates and fragmented data platforms towards scalable cloud-native lakehouse and data warehouse architectures.
- Ensure architecture decisions address scalability, interoperability, resilience, performance, security, cost optimisation and long-term maintainability.

3. Data Engineering, Integration & Data Platform Leadership

- Provide strategic and architectural oversight for batch and near-real-time pipelines, CDC, incremental loading, reconciliation, recovery and reusable engineering frameworks.
- Govern ingestion from enterprise systems such as SAP / S4HANA, Salesforce, SQL and Oracle, APIs, SaaS applications,



files and streaming platforms.
- Set standards for SQL, Python, dbt, ADF, Airflow, Fivetran, Kafka and custom data integration frameworks.
- Guide enterprise data modelling, dimensional modelling, semantic layers and data product design.
- Establish engineering standards for reliability, observability, production support, CI/CD, testing and release management.

4. Data Governance, Security, Quality & Compliance

- Own the enterprise data governance strategy, including data ownership, stewardship, policies, standards and governance forums.
- Establish enterprise approaches for metadata, cataloguing, lineage, classification, master and reference data, and data lifecycle management.
- Define the data quality strategy: critical data elements, quality rules, monitoring, remediation and executive reporting.
- Ensure data platforms and solutions comply with enterprise security, privacy, access-control and regulatory requirements.
- Govern RBAC, data access, encryption, sensitive-data handling and platform security patterns across the data estate.

5. Analytics, BI, AI & Data Products

- Define the strategy for enterprise analytics, BI and self-service data consumption across platforms such as Power BI, Tableau and Streamlit.
- Establish a scalable semantic and consumption architecture that enables trusted, reusable enterprise data products.
- Define the enterprise approach to AI-ready data, including Snowflake Cortex AI, Cortex Agent / Search, Snowflake CoWork, Databricks AI capabilities, Genie and MLflow integrations where applicable.
- Partner with business and technology leaders to identify and scale high-value AI and analytics use cases.
- Ensure AI initiatives are supported by governed, high-quality, discoverable and secure enterprise data.

6. Operating Model, People & Practice Leadership

- Build and lead the enterprise data practice across architecture, engineering, governance, analytics and platform capabilities.
- Define roles, skills, competency models, delivery standards and career development paths for data teams.
- Establish architecture review boards, engineering standards, reusable accelerators and communities of practice.
- Lead capacity planning, portfolio prioritisation, delivery governance and continuous improvement.
- Coach senior architects, engineering leads and delivery leaders, and build a high-performing, outcome-oriented data organisation.

7. Executive Stakeholder, Vendor & Financial Leadership

- Act as the senior data advisor to CIO, CTO, CDO and business leadership, translating technology choices into business outcomes and investment decisions.
- Build strong relationships with business units, enterprise architecture, cybersecurity, infrastructure, application teams and analytics leaders.
- Manage strategic technology partners and vendors, including platform providers and systems integrators.
- Provide oversight of budgets, commercial commitments, licensing, platform consumption and cost optimisation.
- Communicate strategy, risks, dependencies,



roadmap progress and value realisation to executive stakeholders.

8. Delivery Governance & Business Outcomes

- Provide governance across strategic data programmes to ensure delivery against scope, value, architecture, quality, security, timeline and budget.
- Define measurable KPIs for platform adoption, data quality, delivery velocity, reliability, cost efficiency and business value.
- Identify delivery and architecture risks early and drive resolution across organisational boundaries.
- Ensure enterprise data investments produce measurable improvements in decision-making, operational efficiency, customer outcomes and innovation.

Core Technical & Strategic Competencies

- Enterprise data strategy, data architecture and target operating model.
- Cloud data platforms: Snowflake, AWS, Azure / Microsoft Fabric and Databricks.
- Data warehouse and lakehouse architecture and modernisation.
- Data engineering, ETL/ELT, CDC, streaming and integration.
- Data modelling, semantic layer and data product architecture.
- Data governance, metadata, lineage, data quality, security and RBAC.
- SAP / S4HANA, Salesforce, Oracle and SQL, APIs, SaaS and other enterprise data sources.
- dbt, ADF, Airflow, Fivetran, Kafka and custom integration frameworks.
- Power BI, Tableau and Streamlit.
- AI-ready data architecture: Snowflake Cortex AI, Cortex Agent / Search, Databricks AI, Genie and MLflow.
- Platform performance, reliability, observability and cost optimisation.

Qualifications & Experience

- 12–18+ years in data engineering, data architecture, analytics, cloud platforms or enterprise technology transformation.
- Proven experience owning or shaping enterprise-wide data strategy for a large organisation.
- Experience leading large data modernisation or cloud transformation programmes, from strategy through execution.
- Strong understanding of Snowflake architecture and enterprise operating patterns. Databricks and AWS / Azure / Fabric experience is highly desirable.
- Strong knowledge of data governance, security, quality, metadata, architecture and enterprise integration.
- Demonstrated ability to engage CIO, CTO and CDO-level stakeholders and influence senior business and technology leadership.
- Experience managing large multidisciplinary teams, strategic partners, budgets and technology vendors.
- Strong business acumen, with the ability to connect data investments to measurable business outcomes.

Preferred Certifications

- Snowflake SnowPro Core or Advanced: Data Engineer, or equivalent.
- AWS Data Engineer or another relevant AWS certification.
- Microsoft Azure or Fabric data certification.
- Product management or project management certifications.

Success Measures for the Role

- Enterprise data strategy and target architecture approved and adopted by key business and technology stakeholders.
- A clearly prioritised multi-year data roadmap with measurable value, investment and delivery milestones.
- Improved data quality, governance, security, discoverability and trust across critical enterprise data.
- Successful modernisation and rationalisation of the enterprise data platform landscape.
- Improved platform reliability, engineering productivity, performance and cost efficiency.
- Increased adoption of governed enterprise data products, analytics and AI use cases.
- A high-performing data practice with strong architecture, engineering, governance and delivery capabilities.

📌 Lead - Data Engineering (Hyderabad)
🏢 Biological E
📍 Hyderabad