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Data Engineer – Financial Analytics (FP&A)

Bengaluru, KA, India

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What you’ll work on

Full posting

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.

  • Work day to day with the Head of FP&A to understand reporting needs across budgeting, forecasting, month-end close and board reporting.

  • Build and maintain ELT pipelines into BigQuery, using SQL, scheduled queries and orchestration tools (for example Dataform, dbt or Cloud Composer).

  • Design star and snowflake schemas with conformed dimensions (time, entity, account, cost center, product, customer, region) and well-defined fact tables.

From the employer’s posting
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.
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.
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.

What you’ll bring

All qualifications

Core experience

  • Proven experience designing multidimensional (dimensional) data models using Kimball methodology: facts, dimensions, hierarchies and slowly changing dimensions.
  • Ability to work directly with senior finance stakeholders
  • Ability to translate business requirements into technical solutions
  • Experience with dbt or Dataform for version-controlled, tested SQL transformations.
  • Bachelor's degree in Computer Science, Information Systems, Engineering, Finance, Statistics or a related field
  • Hands-on experience integrating data from at least three types of source systems (for example ERP, CRM, flat files, APIs).
Qualification wording
Proven experience designing multidimensional (dimensional) data models using Kimball methodology: facts, dimensions, hierarchies and slowly changing dimensions.
Ability to work directly with senior finance stakeholders
Ability to translate business requirements into technical solutions
Experience with dbt or Dataform for version-controlled, tested SQL transformations.
Bachelor's degree in Computer Science, Information Systems, Engineering, Finance, Statistics or a related field
Hands-on experience integrating data from at least three types of source systems (for example ERP, CRM, flat files, APIs).

Tools in this posting

  • SQL
  • BigQuery
  • dbt
  • Excel
  • Looker
  • SAP
  • Python
  • Google Cloud (GCP)
  • Power BI
  • Power Query
Source — Tool mentions in context
- 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.
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.
Good to Have Skills - Experience with dbt or Dataform for version-controlled, tested SQL transformations. - Familiarity with LookML or a comparable semantic modeling layer.
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
- 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
- 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.
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).
- 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
- 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).
- 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.
- 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.
- 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.
- 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.
- 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.
- 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

Job description

View original posting ↗

Job Description

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

Qualifications

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

Additional Information

UK business hours with overlap till 1 pm EST

Company Description

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

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Bengaluru, KA, India

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Oct 7, 2026
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Last seen by us
Oct 8, 2026

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