Amazon Data Engineer Fintech 2026 – Promising Finance Data Platform Career, Apply Online Now

Amazon Data Engineer Fintech opening for 2026 is a promising full-time career opportunity for passionate data engineers to shape the future of Amazon’s global finance data platform — one of the world’s largest finance data warehouses by volume. Amazon’s Finance Tech (FinTech) team is hiring a Data Engineer to build robust, scalable data models, optimize ETL pipelines, and deliver BI solutions that drive day-to-day financial decision-making across Amazon’s North America, Asia, and Europe operations using SQL, Python, Spark, Hadoop, Hive, EMR, and AWS.

If you have 1+ years of data engineering experience, a passion for solving hard problems at massive scale, and excitement about big data technologies in a global finance context, this outstanding Amazon FinTech data engineering role is your next high-impact career move in 2026.

Job Details

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About Amazon Finance Tech (FinTech)

Amazon’s Finance Tech (FinTech) team is responsible for building the next-generation big data platform that will become one of the world’s largest finance data warehouses by volume, supporting Amazon’s rapidly growing and dynamic global businesses across tax, finance, and accounting functions. This team interfaces with financial stakeholders across North America, Asia, and Europe to deliver BI applications and data-driven insights that have an immediate influence on day-to-day financial decision-making at one of the world’s most valuable companies.

The FinTech data engineering team is known for leveraging the latest big data techniques — Hadoop, Hive, Spark, EMR, and AWS — and for challenging its engineers to think big, move fast, and consistently innovate at scale. Team members share ownership of the technical vision for advanced reporting and insight products, working with top-notch technical professionals building complex financial data systems with a focus on sustained operational excellence.

Eligibility Criteria

• Education: Bachelor’s degree in Computer Science, Engineering, Information Systems, Mathematics, Statistics, or related field — preferred
• Experience: Minimum 1+ years of data engineering experience — mandatory
• Mandatory Technical Skills:
  • SQL — complex query writing, optimization, and large-scale dataset analysis — strictly mandatory
  • Data Modeling — designing scalable data models for financial reporting and analytics
  • Data Warehousing — experience designing and managing cloud or on-premise data warehouses
  • ETL Pipeline Development — building, maintaining, and optimizing extract, transform, and load workflows
  • Query Languages (any one or more): SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, or Scala
  • Scripting Languages (any one or more): Python or KornShell
• Preferred / Good-to-Have Skills:
  • Big Data Technologies: Apache Hadoop, Hive, Spark, and AWS EMR (Elastic MapReduce)
  • ETL Tools: Informatica, ODI (Oracle Data Integrator), SSIS, BODI, or Datastage
  • AWS data services knowledge — Redshift, S3, Glue, Lambda, or Step Functions
  • Experience with BI tools and dashboard development for financial reporting use cases
  • Understanding of finance, tax, and accounting data structures and reporting requirements

Roles & Responsibilities

🔷 Data Platform Design & Engineering

• Design, implement, and support a platform providing secured access to large-scale financial datasets for tax, finance, and accounting customers globally
• Model data and metadata to support both ad-hoc and pre-built financial reporting needs across Amazon’s global business units

🔷 BI Solution Delivery

• Interface with tax, finance, and accounting stakeholders across NA, Asia, and Europe — gather requirements and deliver complete end-to-end BI solutions
• Own the design, development, and maintenance of ongoing metrics, reports, analyses, and dashboards that drive key financial business decisions at Amazon

🔷 Data Quality & Best Practices

• Collaborate with Finance Analysts to recognize and help adopt best practices in reporting and analysis — data integrity, test design, analysis, validation, and documentation
• Continually improve ongoing reporting and analysis processes — automating or simplifying self-service support for financial datasets

🔷 Performance Optimization

• Tune application and query performance using AWS profiling tools and services for large-scale financial data workloads
• Keep up to date with advances in big data technologies and run pilots to design data architecture that scales with increased data volume using AWS

🔷 Problem Solving & Innovation

• Analyze and solve problems at their root, stepping back to understand the broader business context and long-term data architecture implications
• Triage multiple courses of action in high-ambiguity environments using both quantitative analysis and business judgment
• Learn and understand Amazon’s broad data resources — knowing when, how, and which data sources and tools to use for each financial analytics use case

Selection Process

1. Online Application – Apply via Amazon Jobs portal (amazon.jobs) for Data Engineer – Fintech
2. Online Assessment – SQL query writing, data modeling concepts, scripting logic (Python), and analytical reasoning
3. Technical Phone Screen – SQL optimization, ETL design discussion, and Spark/Hadoop fundamentals
4. Technical Interview Loop (4–5 rounds):

5. Hiring Manager Review – Final technical and cultural alignment discussion
6. Offer & Onboarding – Background check, compensation discussion, and joining

How to Apply For Amazon Data Engineer Fintech

1. Visit the official careers page: Link Given Below
2. Click “Apply Now” and sign in or create your Amazon Jobs account
3. Upload your updated resume highlighting SQL expertise, ETL pipelines, data modeling, Spark/Hadoop/Hive experience, Python scripting, and BI reporting projects
4. Complete the online assessment promptly after submitting your application
5. Monitor your registered email for interview scheduling from Amazon’s FinTech recruiting team

Preparation Tips

• Master SparkSQL and HiveQL for financial data at scale — Amazon FinTech processes massive finance datasets using Spark on AWS EMR; practice writing SparkSQL queries for aggregation on billion-row datasets, Hive partitioned table design for tax reporting, and PySpark DataFrame transformations for ETL workflows; understand the performance differences between Spark SQL and MapReduce for financial analytics use cases
• Build a finance-themed big data project on AWS — create an end-to-end FinTech data pipeline: ingest financial transaction data from S3 → process with Spark on EMR → transform with HiveQL → load into Redshift → schedule with Apache Airflow → visualize in QuickSight or Tableau; this directly mirrors Amazon FinTech’s technology stack and gives you concrete technical talking points across every interview round
• Study ETL tool concepts for the preferred qualifications — Amazon lists Informatica, ODI, SSIS, and Datastage as preferred; even high-level understanding of these enterprise ETL platforms — source/target mapping, transformation logic, scheduling, error handling, and lineage tracking — is valuable; practice describing how you would migrate an Informatica-based ETL pipeline to a Spark-based cloud-native alternative
• Develop deep SQL optimization skills for finance queries — Amazon FinTech SQL interviews specifically test performance on complex finance reporting queries; practice: multi-level window aggregations for running totals, LAG/LEAD for period-over-period financial comparisons, ROLLUP/CUBE for hierarchical financial summaries, and execution plan analysis for query optimization in Redshift and Spark SQL environments
• Understand finance data structures for BI delivery — be ready to discuss the data architecture for common finance reporting needs: general ledger fact tables, chart of accounts hierarchies, tax jurisdiction dimensions, currency conversion slowly changing dimensions, and intercompany elimination logic; demonstrating finance domain awareness is a meaningful differentiator in Amazon FinTech interviews beyond pure engineering skills
• Prepare self-service analytics architecture answers — Amazon FinTech specifically mentions simplifying self-service support for datasets; prepare to design a self-service analytics layer: curated data mart design in Redshift → semantic layer with pre-built metrics → BI tool connectivity → row-level security for finance data; this demonstrates end-to-end BI solution thinking valued by Amazon Finance stakeholders
• Build strong Amazon Leadership Principle answers for FinTech context — the LP behavioral round is critical at Amazon; prepare STAR examples specifically relevant to FinTech: Dive Deep (a data quality issue in a financial pipeline you traced to root cause), Deliver Results (a finance dashboard you delivered under tight month-end reporting deadlines), Invent and Simplify (a process you automated that previously required manual finance analyst effort), and Customer Obsession (how your data work directly improved a finance stakeholder’s decision-making capability)

Important Dates

✅ Apply Now via Amazon Jobs! submit your application today. Emphasize your SQL optimization expertise, Spark/Hadoop/Hive experience, Python scripting, ETL pipeline architecture, and AWS data infrastructure knowledge prominently in your resume — these are Amazon FinTech’s primary technical shortlisting criteria for this high-impact global finance data engineering role!

Frequently Asked Questions (FAQ’s)