{"id":2975292,"url":"https://alion.io/job/digirecruitx-financial-data-engineer","title":"Financial Data 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":63,"open_postings":93,"ghost_share":0.613,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-23T05:54:15Z"}},"role":"Data Science","role_family":"Data Science","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":["Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":14500,"max_usd":36000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":21},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Time Series","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"GCP","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Snowflake","optional":true}],"status":"live","first_seen_at":"2026-07-30T05:16:31Z","employer_posted_date":"2026-07-30","last_verified_at":"2026-10-24T00:42:51Z","board_verified":true,"closed_at":null,"days_open":85,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":85},"description":"We are seeking an experienced and detail-oriented Financial Data Engineer to build, manage, and scale high-quality financial data products used by investment and research professionals.\nThe ideal candidate will combine strong financial-domain knowledge with hands-on expertise in Python, SQL, data engineering, and large-scale financial datasets. This role requires a strong focus on data accuracy, process transparency, automation, and end-to-end ownership of the financial data lifecycle.\nKey Responsibilities\nDesign and implement business logic for financial data products, including actuals, consensus estimates, management guidance, pricing, and valuation datasets.\nBuild reliable data pipelines that support downstream platforms such as Excel-based applications, APIs, data feeds, and cloud data warehouses.\nOwn the complete data lifecycle, from sourcing and transformation to validation, delivery, and ongoing quality monitoring.\nWork with complex financial datasets derived from public-company filings, sell-side estimates, market data, and corporate actions.\nApply financial-data concepts such as point-in-time data, security master management, calendarization, restatements, and historical data tracking.\nDesign and document transparent, auditable data processes with clear lineage from source to final output.\nDevelop automated data-quality frameworks, including validation rules, reconciliation checks, exception monitoring, and statistical anomaly detection.\nInvestigate data discrepancies and implement scalable solutions to prevent recurring issues.\nPartner with product, engineering, and business teams to ensure accurate and timely delivery of financial data.\nCommunicate complex data logic, financial concepts, and quality issues clearly to technical and non-technical stakeholders.\nContribute to the continuous improvement of data architecture, processes, controls, and operational standards.\nRequired Skills and Qualifications\nBachelor’s or Master’s degree in Financial Engineering, Quantitative Finance, Mathematics, Statistics, Physics, Computer Science, or a related quantitative discipline.\n4-7 years of experience in financial data engineering, quantitative data, capital markets, financial analytics, or a related field.\nStrong understanding of the end-to-end financial data lifecycle.\nAdvanced proficiency in Python and SQL.\nHands-on experience working with large and complex time-series datasets.\nExperience handling fundamental financial data such as reported actuals, estimates, guidance, financial statements, and valuation metrics.\nFamiliarity with market data, including security prices, corporate actions, and reference data.\nStrong understanding of data modelling, data transformation, data validation, and reconciliation processes.\nAbility to identify data-quality issues and perform detailed root-cause analysis.\nStrong analytical, problem-solving, documentation, and communication skills.\nAbility to work independently and take ownership of business-critical data products.\nFlexibility to collaborate with global teams and work U.S. business hours when required.\nPreferred Qualifications\nCFA, FRM, or CQF certification, completed or in progress.\nExperience working with financial data platforms, capital-markets technology companies, research firms, or investment-management organizations.\nKnowledge of model-risk management or regulatory model-validation frameworks.\nFamiliarity with cloud environments such as AWS, Microsoft Azure, or Google Cloud Platform.\nExperience with cloud data warehouses such as Snowflake or BigQuery.\nExposure to machine-learning techniques used for forecasting, pricing, risk classification, or anomaly detection.\nPhD in a quantitative discipline may be considered in place of some professional experience.\nWork Schedule: Monday to Friday (Flexibility to work during US shifts, when required, in a work-from-home setup.)\nWork Type : 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