{"id":3392197,"url":"https://alion.io/job/systango-technologies-limited-practice-lead-data-engineering-snowflakedatabricks","title":"Practice Lead – Data Engineering (Snowflake/Databricks)","company":{"id":450069,"name":"Systango Technologies Limited","domain":"systango.com","url":"https://alion.io/company/systango-technologies-limited","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Indore, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":28000,"max_usd":59000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":20},"experience_years_min":13,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Airflow","optional":true},{"name":"dbt","optional":true}],"status":"live","first_seen_at":"2026-10-28T10:13:41Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-10-29T12:35:23Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"About Systango\nSystango 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.\nRole Overview\nWe 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.\nThe 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.\nThe 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.\nKey Responsibilities\n1. Practice Strategy & Growth\nDefine and execute the Data Engineering practice roadmap, technology strategy, and capability development plan.\nBuild expertise and reusable solutions around Snowflake, Databricks, lakehouse architecture, cloud data platforms, and modern data engineering.\nIdentify emerging technologies, industry trends, and opportunities to expand the practice.\nContribute to practice growth through client relationships, solution consulting, pre-sales, and new engagement opportunities.\nDevelop accelerators, reusable frameworks, reference architectures, and best practices to improve delivery efficiency.\nAccount Ownership: Own client accounts, manage client relationships, ensure delivery excellence, and identify opportunities for account growth and expansion.\n2. Data Architecture & Technical Leadership\nLead the design of enterprise-grade data platforms using Snowflake, Databricks, and cloud-native services.\nDefine end-to-end architectures for data lakes, lakehouses, modern data warehouses, data pipelines, and analytics platforms.\nEstablish architectural standards for scalability, reliability, security, performance, and cost optimization.\nGuide decisions around batch and streaming processing, ETL/ELT, data modelling, orchestration, and distributed computing.\nReview solution designs, conduct architecture assessments, and resolve complex technical challenges.\nEnsure data platforms are designed to support BI, advanced analytics, AI/ML, and Generative AI use cases.\nPetabyte-Scale Data Engineering: Must have hands-on experience designing and optimizing production data platforms that process and manage petabytes of data.\n3. Client Consulting & Pre-Sales\nEngage with client stakeholders, technology leaders, and business teams to understand their data challenges and objectives.\nLead technical discovery workshops, architecture discussions, solution demonstrations, and technology assessments.\nTranslate business requirements into scalable technical solutions, proposals, estimates, and implementation roadmaps.\nSupport RFP/RFI responses, solution presentations, proof of concepts, and technical due diligence.\nIdentify opportunities for platform modernization, cloud migration, data platform optimization, and analytics transformation.\nClient Discovery & Consulting: Lead client discovery sessions to understand business requirements, identify technical challenges, and translate them into scalable data engineering solutions.\n4. Delivery & Operational Excellence\nProvide technical oversight across multiple data engineering projects and client engagements.\nEstablish delivery standards, architecture governance, code quality practices, and engineering review processes.\nEnsure solutions meet agreed expectations for performance, reliability, security, maintainability, and cost.\nWork with delivery managers and engineering leaders to manage technical risks, dependencies, and escalations.\nDrive continuous improvement through automation, reusable components, and standardized delivery frameworks.\n5. Team Leadership & Capability Building\nBuild, mentor, and develop a high-performing team of data architects, data engineers, technical leads, and consultants.\nDefine technical competency frameworks, learning roadmaps, and certification plans.\nSupport hiring, technical evaluations, career development, and succession planning.\nPromote engineering excellence, knowledge sharing, innovation, and collaborative problem-solving.\nTeam Building & Leadership: Proven experience building, scaling, and leading Data Engineering teams to deliver high-quality data solutions.\nTeam Monitoring & Mentoring: Monitor team performance, mentor junior engineers, conduct code and design reviews, provide technical guidance, and drive continuous technical improvement.\n6. Snowflake & Databricks Expertise\nDrive best practices for Snowflake architecture, workload management, data sharing, performance tuning, and cost governance.\nGuide Databricks implementations involving Apache Spark, PySpark, Delta Lake, lakehouse architecture, and Unity Catalog.\nDefine integration patterns across cloud storage, orchestration tools, data ingestion frameworks, and downstream analytics systems.\nEvaluate platform trade-offs and recommend the right architecture based on client requirements, existing investments, and total cost of ownership.\nApache 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.\nRequired Skills & Experience\n13-18 years of experience in data engineering, data architecture, or enterprise data platform delivery.\nProven experience leading a data engineering practice, capability, business unit, or multiple technical teams.\nStrong hands-on expertise in Snowflake/Databricks, including production-scale implementations and architecture decisions.\nStrong proficiency in SQL, Python, PySpark, distributed data processing, and data pipeline design.\nExpertise in modern data architecture patterns, including data lakes, lakehouses, data warehouses, medallion architecture, and dimensional modelling.\nExperience with AWS, Azure, or GCP and cloud-native data services.\nStrong understanding of ETL/ELT frameworks, orchestration, data integration, data quality, governance, security, and metadata management.\nExperience with platform modernization, cloud migrations, performance optimization, and cost management.\nDemonstrated experience in client-facing consulting, technical solutioning, estimation, and pre-sales support.\nStrong leadership experience in mentoring teams, managing technical escalations, and driving engineering standards.\nExcellent communication, presentation, stakeholder management, and problem-solving skills.\nMandatory Certification: Must hold at least one valid Snowflake or Databricks certification.\nGood to Have\nExperience with dbt, Airflow, Kafka, Delta Lake, Snowpark, Unity Catalog, and modern data observability tools.\nExperience with data mesh, data governance frameworks, and enterprise data strategy.\nExposure to AI/ML platforms, feature engineering, vector search, and GenAI-ready data architectures.\nExperience in FinTech, payments, financial services, or other data-intensive industries.\nExperience building technology practices within an IT services or consulting organization.\nKey Success Metrics\nGrowth of the Data Engineering practice in terms of capability, team strength, and client opportunities.\nSuccessful architecture and delivery of complex Snowflake and Databricks engagements.\nClient satisfaction, technical quality, delivery predictability, and project profitability.\nContribution to pre-sales, proposals, solutioning, and conversion of new engagements.\nImprovement in team productivity, utilization, technical skills, and retention.\nDevelopment of reusable accelerators, reference architectures, and differentiated service offerings.","description_format":"text","description_chars":8063,"description_truncated":false,"requirements":{"experience_years_min":13,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["LLM & Generative AI"],"lifecycle":[{"event":"open","at":"2026-10-28T10:40:12Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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