{"id":3471809,"url":"https://alion.io/job/digirecruitx-data-analytics-reporting-automation-engineer","title":"Data Analytics & Reporting Automation Engineer","company":{"id":2432154,"name":"DigiRecruitx","domain":"digirecruitx.com","url":"https://alion.io/company/digirecruitx","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"WP Job Openings","truth_index":{"grade":"C","score":59,"open_postings":94,"ghost_share":0.691,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-31T05:58:55Z"}},"role":"Analytics","role_family":"Analytics","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":13500,"max_usd":30000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1854},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ETL/ELT","optional":false},{"name":"Looker","optional":false},{"name":"Metabase","optional":false},{"name":"MySQL","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Dimensional Modeling","optional":true},{"name":"GCP","optional":true},{"name":"Git","optional":true},{"name":"Machine Learning","optional":true},{"name":"Rest API","optional":true}],"status":"live","first_seen_at":"2026-10-29T13:29:50Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-10-31T15:39:10Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":1},"description":"We are seeking a skilled Data Analytics & Reporting Automation Engineer with strong expertise in SQL, Python, Business Intelligence (BI), and reporting automation.The ideal candidate will be responsible for transforming complex datasets into meaningful business insights, developing automated reporting solutions, and building interactive dashboards to support data-driven decision-making.This role requires hands-on experience in data extraction, transformation, analysis, visualization, and workflow automation, along with strong analytical and problem-solving abilities.\nKey Responsibilities\n1. Data Extraction & Management\nDevelop, optimize, and maintain complex SQL queries for data extraction and analysis.\nDesign and manage ETL/ELT workflows to integrate data from multiple sources.\nPerform data cleaning, transformation, validation, and aggregation.\nDevelop reusable datasets, database views, stored procedures, and analytical data models.\nIdentify and resolve data quality issues while improving query performance.\n2. Reporting & Workflow Automation\nDesign and automate daily, weekly, and monthly business reports.\nDevelop scheduled workflows for automated data processing, report generation, and distribution.\nIntegrate databases, APIs, spreadsheets, and reporting platforms.\nImplement automated data validation, reconciliation, and exception monitoring.\nTroubleshoot reporting failures and ensure timely, accurate delivery.\n3. Data Analytics & Business Intelligence\nAnalyze business performance, operational KPIs, and historical trends.\nConduct descriptive and diagnostic analysis to identify patterns, anomalies, and performance gaps.\nApply statistical analysis and forecasting techniques for predictive insights.\nExplore prescriptive analytics to recommend improvements and support business decisions.\nTranslate business requirements into measurable KPIs and actionable insights.\n4. Dashboard Development & Visualization\nDesign and maintain interactive dashboards using Power BI, Tableau, Looker, Metabase, or similar BI tools.\nPresent complex information through intuitive charts, visualizations, and performance reports.\nEnsure dashboards are accurate, efficient, scalable, and user-friendly.\nCommunicate analytical findings and business recommendations to stakeholders.\n5. Data Quality & Process Improvement\nEstablish data validation, reconciliation, and reporting quality standards.\nDocument SQL queries, reporting logic, data definitions, and automation workflows.\nIdentify opportunities to reduce manual reporting and improve operational efficiency.\nCollaborate with product, engineering, operations, and business teams to deliver reliable analytics solutions.\nRequired Skills & Qualifications\nBachelor’s degree in Computer Science, IT, Data Science, Statistics, or a related discipline.\n3-5 years of relevant experience in Data Analytics, BI, Reporting Automation, Analytics Engineering, or Data Engineering.\nAdvanced SQL proficiency, including joins, CTEs, window functions, subqueries, aggregations, and query optimization.\nHands-on experience with MySQL, PostgreSQL, SQL Server, or similar relational databases.\nStrong Python skills for data processing, analysis, and automation.\nPractical experience with at least one BI platform such as Power BI, Tableau, Looker, or Metabase.\nExperience developing automated reports and recurring data workflows.\nStrong understanding of data modeling, ETL/ELT processes, data transformation, and validation.\nKnowledge of descriptive, diagnostic, predictive, and prescriptive analytics.\nExcellent analytical, problem-solving, communication, and stakeholder management skills.\nPreferred Skills\nExperience with workflow orchestration tools, scheduled jobs, and automated report distribution.\nFamiliarity with REST APIs and multi-source data integration.\nKnowledge of statistical modeling, regression, forecasting, and basic machine learning.\nUnderstanding of data warehousing and dimensional modeling.\nExposure to AWS, Azure, or Google Cloud data services.\nFamiliarity with Git, testing, monitoring, logging, and deployment practices.\nExperience using AI-assisted tools for analytics and reporting 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