Senior Analytics Engineer (Platform, Financial Analytics)

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 Coinbase