{"id":1252780,"url":"https://alion.io/job/talentgen-advisory-llp-data-engineer","title":"Data Engineer","company":{"id":3800611,"name":"TalentGen Advisory","domain":"talentgen.co.in","url":"https://alion.io/company/talentgen-advisory-llp","size_band":null,"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":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":42000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":56},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Delta Lake","optional":false},{"name":"Embeddings","optional":false},{"name":"GDPR","optional":false},{"name":"Great Expectations","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Azure","optional":true}],"status":"live","first_seen_at":"2026-09-13T19:45:17Z","employer_posted_date":null,"last_verified_at":"2026-09-13T19:45:17Z","board_verified":false,"closed_at":null,"days_open":28,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":28},"description":"Description : \n\nJob Title : Data Engineer\n\nDepartment : Global Analytics\n\nReports to : Manager AI Engineering\n\nLevel : Senior / Medior (depending on experience)\n\nRole Summary : \n\nThe Data Engineer is responsible for designing, building, and operating high-quality, scalable, and reusable data services that support analytics, AI, and GenAI use cases across business domains.\n\nIn this role, you will design and work hands-on with data pipelines, data models, orchestration frameworks, storage layers, and observability tooling.\n\nYou will collaborate closely with AI Engineers, Data Scientists, Product Owners, and Platform teams to deliver reliable, well-governed, and self-service data products.\n\nKey Responsibilities : \n\nData Platform & Services Engineering : \n\n- Build and maintain scalable data pipelines and ingestion frameworks for batch, streaming, and event-driven data.\n\n- Develop and maintain modular data models and semantic layers optimized for analytics, BI self-service and AI use cases.\n\n- Implement and operate orchestration workflows (e.g., Databricks Workflows) and compute engines (Spark, SQL, Python).\n\n- Work with storage technologies such as Delta Lake, ADLS, feature and vector stores.\n\nData Quality, Governance & Observability : \n\n- Implement data quality checks, validations, and monitoring to ensure reliability and trust in data products.\n\n- Contribute to data lineage, metadata management, and documentation.\n\n- Apply observability practices using tools such as Great Expectations or Monte Carlo.\n\n- Ensure compliance with data governance standards and regulations (e.g., GDPR) in collaboration with data governance teams.\n\nEnablement for AI & Analytics Use Cases : \n\n- Deliver curated datasets and reusable data assets for analytics, machine learning, and GenAI applications.\n\n- Build pipelines that process structured, graph, and unstructured data (e.g., text, documents, images).\n\n- Support AI Engineering teams with data preparation for embeddings, vector stores, and retrieval-augmented generation (RAG) pipelines.\n\nTooling & Self-Service : \n\n- Contribute to data engineering tooling and frameworks that enable e icient development and deployment of pipelines.\n\n- Develop data pipelines using tools such as dbt and Databricks Lakeflow.\n\n- Support reuse of data services through clear documentation, data contracts, templates, and examples.\n\nCollaboration & Ways of Working : \n\n- Collaborate with Data Scientists, AI Engineers, Product Owners, Business SMEs, and Platform teams.\n\n- Participate in technical design discussions, code reviews, and architecture forums.\n\n- Follow engineering best practices for version control, testing, CI/CD, and operational excellence.\n\nPreferred Qualifications : \n\n- 5+ years of experience in data engineering and building production-grade data pipelines.\n\n- Strong hands-on experience with data platforms such as Databricks.\n\n- Solid knowledge of data modeling, SQL, Spark, and Python.\n\n- Experience with orchestration frameworks, data quality tooling, and observability practices.\n\n- Exposure to unstructured data processing and AI/GenAI data pipelines is a strong plus.\n\n- Experience working in a global, multi-team environment is beneficial.\n\nSuccess in This Role Means : \n\n- Reliable, well-documented data products are available for analytics and AI use cases.\n\n- Data pipelines are scalable, cost-efficient, observable, and easy to operate.\n\n- Data engineers and AI teams can move faster using reusable patterns and self service data services.\n\n- Structured and unstructured data are effectively integrated to support advanced analytics and GenAI innovation. \nSkills\nData Engineering, Python, SQL, Data Management, Spark, Azure Databricks, Data Pipeline, Data Ingestion, Data 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