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Back to careers RemoteFull-timeData EngineeringUnited States preferredFinancial data infrastructureCloud / ETL / ELT Finstock Careers Data Engineer Build scalable data pipelines, data quality systems, financial datasets, and secure data infrastructure for Finstock's AI-powered financial research and market intelligence platform. Remote - United States preferred Full-time USD $105,000-$140,000/year Role metadata Company Finstock, Inc. Location Remote - United States preferred Type Full-time employee Compensation USD $105,000-$140,000 annual base salary Reporting line Engineering Lead Tools Microsoft Teams, Outlook, Microsoft 365, GitHub, cloud data platforms About Finstock, Inc. Finstock, Inc. builds AI-powered financial research, trading analytics, quantitative research, and market intelligence tools for experienced market users, analysts, research teams, and institutions. Our products rely on clean, reliable, secure, and scalable data infrastructure across market data, financial statements, company disclosures, research workflows, analytics tools, and user-facing product features. About The Role We are hiring a Data Engineer to help build and maintain the data infrastructure behind Finstock's financial research and market intelligence platform. In this role, you will design, develop, test, and operate data pipelines that ingest, clean, transform, validate, and deliver financial and market-related data to internal systems, analyst workflows, AI-assisted research tools, and user-facing product features. This is a remote role with a preference for candidates based in the United States. Candidates in other countries may be considered where Finstock, Inc. is able to engage them in compliance with applicable employment, tax, data protection, and operational requirements. Key Responsibilities Design, build, and maintain scalable data pipelines for financial market data, company fundamentals, filings, corporate actions, news, research metadata, analytics outputs, and product usage data. Develop robust ETL/ELT workflows for batch and near-real-time data processing. Build and maintain data models, data marts, warehouse tables, and analytical datasets used by product, research, AI, and engineering teams. Implement data quality checks, validation rules, reconciliation workflows, anomaly detection, and automated monitoring. Improve data reliability, latency, lineage, observability, and documentation across Finstock's data infrastructure. Integrate data from APIs, vendor feeds, public sources, internal systems, and approved third-party data providers. Collaborate with analysts, product managers, AI engineers, backend engineers, and leadership to translate business and research requirements into reliable data products. Support financial research workflows involving equities, ETFs, indices, FX, crypto assets, commodities, macro indicators, and cross-asset market intelligence. Build secure data access patterns, permission controls, and audit-friendly workflows for sensitive or user-scoped data. Maintain documentation for data sources, schemas, transformation logic, pipeline ownership, data quality assumptions, and known limitations. Troubleshoot pipeline failures, data discrepancies, performance bottlenecks, and production incidents. Contribute to cloud infrastructure, CI/CD workflows, testing standards, and engineering best practices for data systems. Ensure data usage follows applicable licensing, confidentiality, security, privacy, and compliance requirements. Required Qualifications 3+ years of professional experience in data engineering, backend data systems, analytics engineering, or a related technical role. Strong proficiency in SQL and Python. Experience building and maintaining production-grade ETL/ELT pipelines. Experience with cloud platforms such as AWS, Google Cloud Platform, or Microsoft Azure. Experience with data warehouses or lakehouse technologies such as Snowflake, BigQuery, Redshift, Databricks, Delta Lake, or similar systems. Familiarity with orchestration and transformation tools such as Airflow, Dagster, Prefect, dbt, or similar workflow tools. Strong understanding of data modeling, schema design, partitioning, indexing, data quality testing, and pipeline observability. Ability to work with APIs, structured data, semi-structured data, JSON, CSV, Parquet, relational databases, and time-series datasets. Strong debugging, documentation, and communication skills. Ability to work independently in a remote environment and collaborate effectively across product, engineering, and analyst teams. Professional commitment to data security, confidentiality, and responsible handling of financial and user-related data. Ability to work remotely in compliance with applicable laws and eligibility requirements. Preferred Qualifications Experience working with financial market data, trading analytics, investment research platforms, fintech products, or capital markets infrastructure. Familiarity with equities, ETFs, indices, FX, crypto assets, commodities, financial statements, corporate actions, and market data vendors. Experience with streaming or event-driven systems such as Kafka, Kinesis, Pub/Sub, or similar technologies. Experience with data APIs, vector databases, search infrastructure, knowledge graphs, or retrieval systems used in AI-assisted products. Experience with PostgreSQL, MySQL, MongoDB, Redis, Elasticsearch/OpenSearch, or time-series databases. Experience with Docker, Kubernetes, Terraform, GitHub Actions, CI/CD, infrastructure-as-code, and production monitoring tools. Experience supporting AI, machine learning, LLM, or analytics products with reliable data pipelines. Familiarity with data governance, access control, audit trails, privacy controls, and vendor data licensing requirements. Interest in financial research, market intelligence, quantitative analytics, and AI-assisted research workflows. What You Will Work On You May Work On Projects Such As Building market data ingestion pipelines for cross-asset research workflows. Creating clean and reliable datasets for financial analytics, charts, dashboards, and AI-assisted research features. Designing data quality checks for prices, fundamentals, filings, corporate actions, and macroeconomic data. Improving data freshness, pipeline monitoring, lineage, and alerting. Supporting internal analyst workflows with curated financial datasets and automated reporting layers. Building secure user-scoped data workflows for local workspace features and product personalization. Helping engineering and product teams scale data infrastructure as the platform grows. What You Will Gain Opportunity to build data infrastructure for an AI-powered financial research and market intelligence platform. Direct collaboration with product, engineering, AI, and regional analyst teams. Exposure to financial market data, quantitative analytics, research workflows, and AI-assisted product development. Remote work with a distributed international team. A company email account and access to approved work tools, including Microsoft 365, Outlook, Teams, GitHub, and company-approved productivity tools, subject to internal security and usage policies. Opportunity to participate in company offsite activities, including possible Hong Kong offsites, subject to business schedule, travel eligibility, visa/documentation requirements, and company approval. Approved business-related travel, accommodation, and reasonable expenses will be covered by the company. Important Role Boundaries This is a data engineering role supporting financial research infrastructure and product data systems. The role does not require or permit the employee to: Provide personalized investment, legal, tax, accounting, or financial advice to users or clients. Recommend that any individual buy, sell, or hold a security based on personal circumstances. Execute
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
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When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.