S'INSCRIRE

Offres d'emploi

Trouvez des postes près de chez vous, sur site, hybrides ou à distance.
  • Emplois similaires à : Senior Analytics Engineer AI-Driven Analytics Platform (Remote)
XX
Senior Analytics Engineer (Platform, Financial Analytics)Coinbase • New York, New York, United States
Postuler Maintenant
XX

Senior Analytics Engineer (Platform, Financial Analytics)

Coinbase
  • US
    New York, New York, United States
  • US
    New York, New York, United States
Postuler Maintenant

À propos

Requirements
Strong understanding of best practices for designing modular and reusable data models (e.g., star schemas, snowflake schemas). Experience designing curated datasets for analytics/reporting with clear definitions and change management
Proficiency in advanced SQL techniques for data transformation, querying, and optimization
Expertise in scripting and automation, with experience in Object-Oriented Programming (OOP) and building scalable frameworks
Strong ability to translate technical concepts into business value for cross-functional stakeholders. Proven ability to manage projects and communicate effectively across teams
Strong cross-functional communication skills and ability to work effectively with Finance/Accounting partners and navigate ambiguity
Experience building, maintaining, and optimizing ETL/ELT pipelines, using modern tools like dbt, Airflow, or similar. Experience orchestrating data workflows with Airflow (DAG design, scheduling patterns, backfills, operational ownership)
Proficiency in building polished dashboards using tools like Looker, Tableau, Superset, or Python visualization libraries (Matplotlib, Plotly)
Familiarity with version control (GitHub), CI/CD, and modern development workflows
Knowledge of modern data lake/warehouse architectures (e.g., Snowflake, Databricks) and transformation frameworks. Hands-on experience with Snowflake and/or Databricks in production environments
Track record of building for correctness and reliability: data quality frameworks, monitoring/alerting, incident response, and stakeholder-facing SLAs
Ability to understand and address business challenges through analytics engineering
Familiarity with statistics and probability
Expertise in prompt engineering and design for LLMs (e.g., GPT), including creating, refining, and optimizing prompts to improve response accuracy, relevance, and performance for internal tools and use cases
Demonstrate the ability to responsibly use generative AI tools and copilots (e.g., LibreChat, Gemini, Glean) in daily workflows, continuously learn as tools evolve, and apply human-in-the-loop practices to deliver business-ready outputs and drive measurable improvements in efficiency, cost, and quality
(Desirable) Experience with financial reconciliation, controllership/accounting reporting, audit/SOX-style controls, or regulated environments
(Desirable) Familiarity with ledger/event-based financial models and concepts like double-entry accounting
(Desirable) Experience with streaming/event-driven systems (e.g., Kafka/Kinesis) and/or near-real-time data validation patterns
(Desirable) Experience with table replication/synchronization patterns between lakehouse and warehouse environments
(Desirable) Fintech/crypto domain experience
What the job involves
The Finance Analytics team bridges the gap between data engineering, data science, and business analytics by building scalable, impactful data solutions that empower Finance, Accounting/Controllership, and Treasury stakeholders to make data-driven decisions
We bring deep domain knowledge spanning accounting, business controllership, SOX compliance, and internal audit - ensuring our pipelines, data models, and certified financial datasets meet the rigor and traceability demands of a regulated financial environment
This role supports the data layer efforts that underpin these datasets and the controls/observability needed to keep them reliable
This is a hybrid Data Engineer/Data Scientist/Business Analyst role that has the expertise to understand data flows end to end, and the engineering toolkit to extract the most value out of it indirectly (building tables) or directly (solving problems, delivering insights)
Be the expert: Quickly build subject matter expertise in a specific business area and data domain. Understand the data flows from creation, ingestion, transformation, and delivery
Step into a new line of business and work with Engineering and Product partners to deliver first data pipelines and insights
Communicate with engineering teams to fix data gaps for downstream data users
Take initiative and accountability for fixing issues anywhere in the stack
Perform reconciliation-style validation across sources (internal systems and/or external statements/vendors), identifying discrepancies and driving fixes with upstream owners
Generate business value: Interface with stakeholders on data and product teams to deliver the most commercial value from data (directly or indirectly)
Build curated data models that streamline ledger verification and accounting workflows, helping finance teams accelerate time-to-close for new product launches
Leverage deep understanding of the reconciliation engine alongside statistical and data expertise to propose engineering improvements that drive faster execution and higher match accuracy
Work with PMs to tie together new x-PG, and x-Product data into one holistic framework to optimize key financing product business metrics
Collaborate cross-functionally with Finance/Accounting to translate requirements into durable data models, and with upstream engineering teams to improve source data contracts
Focus on outcomes not tools: Use a variety of frameworks and paradigms to identify the best-fit tools to deliver value
Develop new abstractions (e.g. UDFs, Python packages, dashboards) to support scalable data workflows/infra
Stand up a framework for debugging AI skills/data apps internally, enabling other non-tech stakeholders to quickly add value
Use established tools with mastery (e.g. Google Sheets, SQL) to quickly deliver impact when speed is top priority
Ensure financial correctness & reliability: Implement strong data quality guarantees (tests, monitoring, alerting, SLAs) and partner with stakeholders to define "done" for financial correctness. Improve reliability and operational excellence for critical pipelines (incident response, retro/action items, performance tuning, cost optimization)
#J-18808-Ljbffr
  • New York, New York, United States

Compétences linguistiques

  • English
Avis aux utilisateurs

Cette offre provient d’une plateforme partenaire de TieTalent. Cliquez sur « Postuler maintenant » pour soumettre votre candidature directement sur leur site.