Data Engineer

Practice Lead – Data Practitioner

Location: Hyderabad

Department: Data & AI Strategy

Job Summary:

We are seeking a dynamic and technically accomplished Practice Lead – Data Engineering & Data Science to lead the development of scalable, high-performance data platforms and advanced analytics capabilities across the enterprise. This role combines deep technical expertise in modern data engineering with a strong foundation in data science to enable data-driven innovation, operational efficiency, and AI readiness.

You will be responsible for building a robust data practice, establishing engineering and modeling standards, and empowering cross-functional teams to deliver production-grade data solutions that are secure, explainable, and impactful.

Key Responsibilities:

Data Platform Strategy & Innovation

- Define and execute a modern data engineering and analytics strategy aligned with enterprise AI and digital transformation goals.

- Architect scalable, cloud-native data platforms using technologies like Spark, Kafka, Delta Lake, and Snowflake.

- Drive adoption of data mesh, lakehouse, and real-time streaming architectures.

Practice Building & Talent Development

- Build and scale a high-performing team of data engineers, data scientists, and ML engineers.

- Design internal training programs and career pathways to upskill talent in data engineering, ML modeling, and analytics.

- Establish a “ready-to-deploy” talent pool to support cross-functional data and AI initiatives.

Engineering & Delivery Enablement

- Lead the development of reusable data pipelines, feature stores, and model training workflows.

- Implement CI/CD and MLOps practices for data and ML lifecycle automation.

- Promote modular, reusable components for data ingestion, transformation, and model deployment.

Advanced Analytics & Data Science Enablement

- Enable rapid experimentation and deployment of predictive models, optimization algorithms, and statistical analyses.

- Foster collaboration between data scientists and engineers to operationalize models at scale.

- Support use cases across customer intelligence, operations, finance, and product analytics.

Governance, Quality & Responsible Data Use

- Establish enterprise-wide data governance, lineage, and quality frameworks.

- Ensure compliance with data privacy regulations (e.g., GDPR, HIPAA) and ethical AI standards.

- Serve as a key stakeholder in data governance councils and model risk committees.

Qualifications:

Required:

- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Statistics, or a related field.

- 10+ years of experience in data engineering, analytics, or data science roles.

- Proven leadership in building and scaling data and ML engineering teams.

- Hands-on expertise with tools like Apache Spark, Airflow, dbt, MLflow, TensorFlow/PyTorch, and cloud data platforms (AWS/GCP/Azure).

- Strong understanding of data modeling, distributed systems, and ML lifecycle management.

Preferred:

- Experience in building Data & AI Centers of Excellence or technical communities of practice.

- Familiarity with GenAI, feature engineering at scale, and real-time inference systems.

- Certifications in cloud data services and MLOps platforms.

- Recognized thought leadership in data engineering or data science (e.g., speaking engagements, publications, OSS contributions).