Practice Lead – Data Engineering (Snowflake/Databricks)

About Systango

Systango is a technology company listed on the NSE, delivering digital transformation and technology solutions across Generative AI, AI/ML, Data Engineering, Analytics, Blockchain/Web3, Cloud, and FinTech. We partner with global clients to design, build, and scale innovative technology solutions.

Role Overview

We are looking for an experienced Practice Lead - Data Engineering to build, lead, and scale our data engineering practice, with deep expertise in Snowflake, Databricks, modern cloud data platforms, and enterprise data architecture.

The ideal candidate will combine strong technical and architectural capabilities with business acumen, client consulting, delivery leadership, and team development. This person will be responsible for defining the practice roadmap, establishing engineering standards, guiding complex client engagements, supporting pre-sales, and building a high-performing team.

The role requires a hands-on technology leader who can engage with clients at the architecture and business level while guiding engineering teams through implementation and production delivery.

Key Responsibilities

1. Practice Strategy & Growth

• Define and execute the Data Engineering practice roadmap, technology strategy, and capability development plan.
• Build expertise and reusable solutions around Snowflake, Databricks, lakehouse architecture, cloud data platforms, and modern data engineering.
• Identify emerging technologies, industry trends, and opportunities to expand the practice.
• Contribute to practice growth through client relationships, solution consulting, pre-sales, and new engagement opportunities.
• Develop accelerators, reusable frameworks, reference architectures, and best practices to improve delivery efficiency.
• Account Ownership: Own client accounts, manage client relationships, ensure delivery excellence, and identify opportunities for account growth and expansion.

2. Data Architecture & Technical Leadership

• Lead the design of enterprise-grade data platforms using Snowflake, Databricks, and cloud-native services.
• Define end-to-end architectures for data lakes, lakehouses, modern data warehouses, data pipelines, and analytics platforms.
• Establish architectural standards for scalability, reliability, security, performance, and cost optimization.
• Guide decisions around batch and streaming processing, ETL/ELT, data modelling, orchestration, and distributed computing.
• Review solution designs, conduct architecture assessments, and resolve complex technical challenges.
• Ensure data platforms are designed to support BI, advanced analytics, AI/ML, and Generative AI use cases.
• Petabyte-Scale Data Engineering: Must have hands-on experience designing and optimizing production data platforms that process and manage petabytes of data.

3. Client Consulting & Pre-Sales

• Engage with client stakeholders, technology leaders, and business teams to understand their data challenges and objectives.
• Lead technical discovery workshops, architecture discussions, solution demonstrations, and technology assessments.
• Translate business requirements into scalable technical solutions, proposals, estimates, and implementation roadmaps.
• Support RFP/RFI responses, solution presentations, proof of concepts, and technical due diligence.
• Identify opportunities for platform modernization, cloud migration, data platform optimization, and analytics transformation.
• Client Discovery & Consulting: Lead client discovery sessions to understand business requirements, identify technical challenges, and translate them into scalable data engineering solutions.

4. Delivery & Operational Excellence

• Provide technical oversight across multiple data engineering projects and client engagements.
• Establish delivery standards, architecture governance, code quality practices, and engineering review processes.
• Ensure solutions meet agreed expectations for performance, reliability, security, maintainability, and cost.
• Work with delivery managers and engineering leaders to manage technical risks, dependencies, and escalations.
• Drive continuous improvement through automation, reusable components, and standardized delivery frameworks.

5. Team Leadership & Capability Building

• Build, mentor, and develop a high-performing team of data architects, data engineers, technical leads, and consultants.
• Define technical competency frameworks, learning roadmaps, and certification plans.
• Support hiring, technical evaluations, career development, and succession planning.
• Promote engineering excellence, knowledge sharing, innovation, and collaborative problem-solving.
• Team Building & Leadership: Proven experience building, scaling, and leading Data Engineering teams to deliver high-quality data solutions.
• Team Monitoring & Mentoring: Monitor team performance, mentor junior engineers, conduct code and design reviews, provide technical guidance, and drive continuous technical improvement.

6. Snowflake & Databricks Expertise

• Drive best practices for Snowflake architecture, workload management, data sharing, performance tuning, and cost governance.
• Guide Databricks implementations involving Apache Spark, PySpark, Delta Lake, lakehouse architecture, and Unity Catalog.
• Define integration patterns across cloud storage, orchestration tools, data ingestion frameworks, and downstream analytics systems.
• Evaluate platform trade-offs and recommend the right architecture based on client requirements, existing investments, and total cost of ownership.
• Apache Kafka & Real-Time Streaming: Must have strong hands-on experience with Kafka, real-time data streaming, event-driven architectures, and high-throughput distributed data processing.

Required Skills & Experience

• 13-18 years of experience in data engineering, data architecture, or enterprise data platform delivery.
• Proven experience leading a data engineering practice, capability, business unit, or multiple technical teams.
• Strong hands-on expertise in Snowflake/Databricks, including production-scale implementations and architecture decisions.
• Strong proficiency in SQL, Python, PySpark, distributed data processing, and data pipeline design.
• Expertise in modern data architecture patterns, including data lakes, lakehouses, data warehouses, medallion architecture, and dimensional modelling.
• Experience with AWS, Azure, or GCP and cloud-native data services.
• Strong understanding of ETL/ELT frameworks, orchestration, data integration, data quality, governance, security, and metadata management.
• Experience with platform modernization, cloud migrations, performance optimization, and cost management.
• Demonstrated experience in client-facing consulting, technical solutioning, estimation, and pre-sales support.
• Strong leadership experience in mentoring teams, managing technical escalations, and driving engineering standards.
• Excellent communication, presentation, stakeholder management, and problem-solving skills.
• Mandatory Certification: Must hold at least one valid Snowflake or Databricks certification.

Good to Have

• Experience with dbt, Airflow, Kafka, Delta Lake, Snowpark, Unity Catalog, and modern data observability tools.
• Experience with data mesh, data governance frameworks, and enterprise data strategy.
• Exposure to AI/ML platforms, feature engineering, vector search, and GenAI-ready data architectures.
• Experience in FinTech, payments, financial services, or other data-intensive industries.
• Experience building technology practices within an IT services or consulting organization.

Key Success Metrics

• Growth of the Data Engineering practice in terms of capability, team strength, and client opportunities.
• Successful architecture and delivery of complex Snowflake and Databricks engagements.
• Client satisfaction, technical quality, delivery predictability, and project profitability.
• Contribution to pre-sales, proposals, solutioning, and conversion of new engagements.
• Improvement in team productivity, utilization, technical skills, and retention.
• Development of reusable accelerators, reference architectures, and differentiated service offerings.