Data Engineer – Financial Analytics (FP&A)

Engineering the AI-powered enterprise. With AI and cloud-native solutions, BETSOL accelerates cloud transformation for enterprises across 17+ countries. BETSOL holds several engineering patents, and is recognized with industry awards. BETSOL maintains a net promoter score that is 2x the industry average.  

BETSOL’s open source backup and recovery product line, Zmanda (Zmanda.com), delivers up to 50% savings in total cost of ownership (TCO) and delivers best-in-class performance.  

BETSOL Global IT Services (BETSOL.com) builds and supports end-to-end enterprise solutions, reducing time-to-market for customers.  

We take pride in being an employee-centric organization, offering comprehensive benefits and opportunities.

Learn more at betsol.com

About the Role

We are hiring a mid-level Data Engineer to build the financial data foundation behind executive reporting, working directly with the Head of FP&A on Google BigQuery. This role turns scattered financial and operational data into a single, trusted reporting model that leadership uses to run the business. You will act as the technical partner to the Head of FP&A, translating planning, forecasting and performance questions into well-designed data models and repeatable metrics. You will own the path from source systems to executive dashboards: gathering data from ERP, CRM, billing, HR and spreadsheet sources, modeling it in BigQuery, and delivering metrics such as revenue, gross margin, operating expense, budget-versus-actual variance and cash position. The aim is to reduce manual spreadsheet effort in monthly close and planning cycles, and to give leadership faster, consistent answers.

Responsibilities

Partnering with FP&A

• Work day to day with the Head of FP&A to understand reporting needs across budgeting, forecasting, month-end close and board reporting.
• Translate business questions into data requirements, metric definitions and model designs, and document them clearly.
• Support planning cycles with timely, reconciled data and ad hoc analysis for leadership requests.

Data integration and transformation

• Gather data from multiple sources such as ERP, CRM, billing, payroll/HR systems, bank feeds and Excel or Google Sheets workbooks.
• Build and maintain ELT pipelines into BigQuery, using SQL, scheduled queries and orchestration tools (for example Dataform, dbt or Cloud Composer).
• Cleanse, standardize and reconcile data, including chart-of-accounts mapping, currency conversion and intercompany eliminations.

Multidimensional data modeling

• Design star and snowflake schemas with conformed dimensions (time, entity, account, cost center, product, customer, region) and well-defined fact tables.
• Handle slowly changing dimensions, account hierarchies, fiscal calendars and actuals-versus-budget-versus-forecast scenarios.
• Optimize BigQuery models for cost and performance through partitioning, clustering and materialized views.

Financial metrics and reporting

• Build a governed semantic layer of financial KPIs: revenue, ARR/MRR where relevant, gross margin, EBITDA, OpEx by function, headcount cost, cash flow and variance analysis.
• Deliver reporting models that feed executive dashboards in Looker, Looker Studio, Power BI or Connected Sheets.
• Produce Excel-ready outputs and pivot-friendly extracts for finance users who work in spreadsheets.

Data quality and governance

• Implement reconciliation checks between source systems, the general ledger and reported figures.
• Apply access controls appropriate to sensitive financial data and maintain a data dictionary for every published metric.
• Monitor pipeline health and resolve data issues before they reach leadership reports.

Looking For

• 4-8 years of experience in Data Engineering or Analytics Engineering.
• Minimum 2 years of hands-on experience working with Google BigQuery 
• Strong experience building data pipelines, dimensional data models, and financial reporting solutions.
• Experience working with finance, FP&A, or business analytics teams.
• Strong understanding of financial reporting concepts such as P&L, Balance Sheet, Cash Flow, Budget vs Actuals, and Forecast Variance analysis. 

Mandatory Skills

Technical Skills

• Expert SQL: complex joins, window functions, CTEs, aggregations, query tuning and BigQuery-specific features (partitioning, clustering, nested and repeated fields, scheduled queries).
• Proven experience designing multidimensional (dimensional) data models using Kimball methodology: facts, dimensions, hierarchies and slowly changing dimensions.
• Advanced Excel: Power Query, pivot tables, XLOOKUP/INDEX-MATCH, dynamic arrays and building finance-ready reporting templates.
• Hands-on experience integrating data from at least three types of source systems (for example ERP, CRM, flat files, APIs).
• Working knowledge of financial statements and FP&A concepts: P&L, balance sheet, cash flow, chart of accounts, budget-versus-actual and forecast variance.
• Experience with at least one BI tool such as Looker, Looker Studio or Power BI.
• Clear communicator who can explain data logic to finance leaders and document metric definitions precisely.

Soft Skills

• Strong analytical and problem-solving skills
• Ability to work directly with senior finance stakeholders
• Clear communicator who can explain data logic to finance leaders and document metric definitions precisely.
• Stakeholder management and business partnering capability
• Ability to translate business requirements into technical solutions

Good to Have Skills

• Experience with dbt or Dataform for version-controlled, tested SQL transformations.
• Familiarity with LookML or a comparable semantic modeling layer.
• Exposure to ERP finance modules (for example NetSuite, SAP, Oracle or Microsoft Dynamics) and planning tools (for example Anaplan, Adaptive Planning or Pigment).
• Python for data processing, API ingestion or automation.
• Experience with Git, CI/CD and Google Cloud services such as Cloud Storage, Cloud Functions and Cloud Composer.
• Google Cloud Professional Data Engineer certification.
• Prior work in a SaaS, IT services or multi-entity, multi-currency business

Bachelor's degree in Computer Science, Information Systems, Engineering, Finance, Statistics or a related field

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