{"id":3315948,"url":"https://alion.io/job/crisil-data-engineering","title":"Data Engineering","company":{"id":1803904,"name":"CRISIL","domain":"crisil.com","url":"https://alion.io/company/crisil","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":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19000,"max_usd":41000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AWS","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Platform Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Azure","optional":true},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-10-26T22:53:50Z","employer_posted_date":null,"last_verified_at":"2026-10-26T22:53:50Z","board_verified":false,"closed_at":null,"days_open":8,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":8},"description":"Data Engineering Manager About the RoleWe are seeking a Data Engineering Manager to help design, build, and optimise our next-generation data platform on Databricks. This is a hands-on engineering role for someone who can work across modern data architecture, scalable data pipelines, platform engineering, and data governance.\nYou will play a key role in migrating from legacy data platforms to Databricks, working closely with architecture, product, technology, and business teams to turn complex requirements into robust, secure, and scalable data solutions.\nThe successful candidate will bring deep technical experience in data engineering, strong Databricks capability, and the ability to influence engineering standards and delivery quality across a modern data platform environment.\n What the Job InvolvesWorking as part of the Data Transformation Team, you will be responsible for:\nDesigning, developing, and optimising scalable data pipelines using Databricks, Apache Spark, Delta Lake, Python, and SQL.\nBuilding reliable ETL and ELT processes that support analytics, reporting, benchmarking, and data product use cases.\nContributing to the design and implementation of the Databricks Lakehouse architecture, including data modelling, data quality, performance, and scalability.\nSupporting the migration of data workloads from legacy platforms to Databricks, working with architects and engineering teams to recommend practical technical solutions.\nImplementing data governance and security controls using Databricks Unity Catalog, including access control, lineage, and environment-level governance.\nImproving the performance and cost efficiency of Databricks clusters, SQL warehouses, workloads, and serverless compute.\nApplying DevOps and DataOps practices, including CI/CD, automated deployment, version control, testing, and infrastructure as code.\nWorking with tools such as Terraform, GitHub Actions, Azure DevOps, or equivalent engineering toolchains.\nCollaborating with Data Transformation, Architecture, Product, Technology, and business stakeholders to deliver high-quality data solutions.\nSupporting engineering best practice through code reviews, technical documentation, design input, and knowledge sharing.\nHelping to establish repeatable engineering patterns for data ingestion, transformation, orchestration, testing, monitoring, and release management.\n RequirementsWe are looking for someone with:\n7 to 10 years experience in data engineering, including strong hands-on experience designing and building production-grade data pipelines.\nSignificant experience working with Databricks, ideally in a platform migration, modernisation, or Lakehouse implementation context.\nDeep expertise in Apache Spark, including PySpark, Spark SQL, and ideally Scala.\nStrong knowledge of Delta Lake and Lakehouse design patterns.\nAdvanced proficiency in Python and SQL.\nExperience working with cloud data platforms and native cloud data services, ideally AWS.\nPractical experience implementing data governance patterns, ideally using Databricks Unity Catalog.\nExperience with infrastructure as code, particularly Terraform or similar tools.\nGood understanding of CI/CD, automated testing, deployment pipelines, and modern engineering practices.\nKnowledge of data warehousing concepts, including dimensional modelling, Kimball methodology, star schemas, and snowflake schemas.\nExperience integrating dbt or similar transformation frameworks with modern data platforms.\nStrong problem-solving skills and the ability to translate complex technical challenges into clear, practical solutions.\nExcellent communication skills, including the ability to explain technical topics to both engineering and non-technical stakeholders.\nExperience working in scaled Agile or product-led delivery environments.\nA strong focus on quality, maintainability, documentation, and engineering discipline.\n Nice to HaveIt would be advantageous if you have:\nDatabricks Certified Data Engineer Professional certification.\nDatabricks Certified Lakehouse Platform Architect certification.\nFamiliarity with MLOps or machine learning engineering workflows.\nExperience working within a Product Development Life Cycle.\nExperience supporting distributed teams across time zones.\nExposure to Jobs-To-Be-Done or Human-Centred Design approaches.\nIndustry experience in financial services, benchmarking, market analytics, or data analytics.\nFamiliarity with scaled Agile ceremonies such as PI Planning.\nA team-oriented, proactive, analytical working style, with the ability to operate across detailed engineering work and broader platform context.","description_format":"text","description_chars":4622,"description_truncated":false,"requirements":{"experience_years_min":7,"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":["Credit Bureaus & Rating Agencies","Financial Data & Market 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