{"id":2802776,"url":"https://alion.io/job/pi-labs-technical-project-lead-data-engineering","title":"Technical Project Lead - Data Engineering","company":{"id":3800783,"name":"Pi-labs","domain":"pi-labs.ai","url":"https://alion.io/company/pi-labs-2","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":27000,"max_usd":46000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":16},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Cassandra","optional":false},{"name":"CI/CD","optional":false},{"name":"ClickHouse","optional":false},{"name":"Docker","optional":false},{"name":"ElasticSearch","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"LLM","optional":false},{"name":"OpenSearch","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"Scrum","optional":false}],"status":"live","first_seen_at":"2026-07-28T10:32:18Z","employer_posted_date":null,"last_verified_at":"2026-07-28T10:32:18Z","board_verified":false,"closed_at":null,"days_open":82,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":82},"description":"Designation : Project Lead\n\nExperience : 6+ Years\n\nLocation : Pune, India / Travel to Customer Site / Hybrid, based on project needs\n\nRole Purpose : \n\nOwn end-to-end delivery and deployments for customer implementations, including planning, technical coordination, data onboarding, integration, deployment readiness, customer engagement, and post-go-live stabilization. The role requires strong Python and data engineering expertise combined with team leadership and customer-facing delivery ownership.\n\nKey Responsibilities : \n\n- Lead end-to-end implementation and deployment of solutions across customer environments, including edge, on-premises, private cloud, and enterprise deployments.\n\n- Own delivery plans, milestones, dependencies, risks, issues, acceptance criteria, and go-live readiness.\n\n- Coordinate Python, data engineering, AI/LLM, DevOps, UI, QA, and integration teams to ensure timely execution.\n\n- Translate customer requirements into implementation workstreams, technical tasks, configuration requirements, and delivery priorities.\n\n- Lead data onboarding activities including source assessment, data mapping, ingestion readiness, schema validation, transformation requirements, and data-quality checks.\n\n- Provide technical guidance on Python services, APIs, ETL/ELT pipelines, data processing, workflow orchestration, and integration patterns.\n\n- Drive deployment planning across infrastructure provisioning, environment configuration, access controls, security prerequisites, release management, and production validation.\n\n- Act as the primary customer-facing delivery lead for implementation discussions, technical workshops, status reviews, issue resolution, and operational handover.\n\n- Manage customer expectations, communicate delivery status clearly, and escalate risks or dependencies proactively.\n\n- Lead onsite or remote deployment activities, including installation, configuration, user acceptance testing, training coordination, and support.\n\n- Review delivery quality, validate technical outputs, and ensure that implemented features meet agreed functional and operational requirements.\n\n- Establish implementation documentation including deployment runbooks, integration specifications, configuration guides, UAT checklists, release notes, and handover documents.\n\n- Mentor implementation engineers and ensure appropriate task allocation, technical review, accountability, and delivery discipline across the project team.\n\n- Support pre-sales and solutioning teams when required for implementation estimation, deployment architecture, technical feasibility, and customer workshops.\n\nRequired Technical Skills : \n\n- Strong hands-on experience in Python development for APIs, automation, backend services, data processing, or integration services.\n\n- Strong understanding of data engineering concepts including ETL/ELT pipelines, data validation, transformation, orchestration, data modelling, and ingestion frameworks.\n\n- Experience working with databases such as PostgreSQL, ClickHouse, Elasticsearch/OpenSearch, MongoDB, Cassandra, or similar platforms.\n\n- Exposure to API integration, REST services, message queues, event-driven processing, and enterprise system integration.\n\n- Understanding of Linux environments, Docker, Kubernetes, CI/CD pipelines, deployment automation, and environment management.\n\n- Familiarity with cloud, on-premises, edge deployment, or air-gapped deployment environments.\n\n- Experience with data platforms, analytics systems, Knowledge Graphs, AI/LLM platforms, or investigation/intelligence platforms is an advantage.\n\n- Ability to review logs, troubleshoot deployment issues, identify root causes, and coordinate resolution across engineering teams.\n\nLeadership and Delivery Skills : \n\n- Demonstrated experience leading cross-functional technical teams.\n\n- Strong ownership mindset with the ability to independently drive projects from kick-off to production handover.\n\n- Ability to manage multiple workstreams, customer dependencies, technical risks, and changing priorities.\n\n- Good understanding of Agile delivery, sprint planning, backlog management, release planning, and UAT coordination.\n\n- Ability to create structured project plans, implementation trackers, issue logs, risk registers, and executive status reports.\n\n- Strong decision-making capability under delivery pressure.\n\n- Customer Handling Skills.\n\n- Excellent verbal and written communication skills.\n\n- Comfortable leading customer workshops, implementation discussions, technical walkthroughs, and governance meetings.\n\n- Ability to explain technical concepts clearly to business users, customer IT teams, leadership stakeholders, and implementation partners.\n\n- Strong stakeholder management and conflict-resolution capability.\n\n- Ability to manage customer expectations while protecting delivery timelines, scope, and quality.\n\nQualification : \n\nBachelors or Masters degree in Computer Science, Information Technology, Data Engineering, Software Engineering, or related discipline.\n\nCertifications in Project Management, Agile, Scrum, Cloud, DevOps, or Data Engineering are preferred but not mandatory.\n\nPreferred Experience : \n\nExperience in enterprise product implementation, SaaS deployment, digital transformation, analytics platforms, intelligence platforms, or data platform deployments.\n\nExperience handling government, enterprise, defence, law-enforcement, telecom, BFSI, or regulated customer deployments is an advantage.\n\nExperience managing distributed teams and customer-site implementation teams is preferred.\n\nSkills\nPython, Data Engineering, Artificial Intelligence, ETL, Data Validation, Data Pipeline, Project Management, IT Project Delivery, Project Implementation, Data Ingestion, Agile","description_format":"text","description_chars":5747,"description_truncated":false,"requirements":{"experience_years_min":6,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Law Enforcement","Cybersecurity 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