Data Engineer

Role: Staff Technical Data Analyst

Location: Mountain View CA/ Remote

Duration: Long Term

Client: 1898 & co

Job Description:

As the Staff Level Data Analyst, you will be the primary driver for transforming our understanding and use of workplace tool data.

You are expected to independently scope, design, and execute complex, ambiguous analytical projects from start to finish.

Your responsibilities will include:

Key Responsibilities

• Data Engineering & Integration Design, implement, and manage scalable data pipelines using Intuit s paved path tools (e.g., Databricks, Delta Lake, Airflow). Ingest, profile, and join datasets from multiple enterprise systems (e.g., Workday, SNOW, UEM, EIM etc) and SAAS tools, to model a holistic view of digital worker behavior.
• Create reusable data assets including synthetic user profiles that integrate system access logs, usage telemetry, and organizational metadata.
• Dataset Modeling & Automation Translate ambiguous business questions into scalable, flexible data structures aligned with change management objectives.
• Build and maintain governed datasets, ensuring quality, security, and consistency. Automate data transformations and outputs that support use cases like feedback loop tracking, engagement and productivity improvement scoring, and identify opportunities for innovative Agentic AI capabilities.
• Dashboard Development & Data Visualization Create clear, maintainable dashboards (using Qlik Sense or similar) that support cross-org visibility into adoption trends, behavioral KPIs, and change sentiment.
• Collaborate with partners to define and evolve data metrics and visualization standards.
• Enable self-service access to curated datasets and BI layers through governed data marts.
• Collaboration & Cross-Functional Enablement Partner with data stewards and product/data teams across domains to source and align data inputs.
• Influence technical decisions around data design, API access, and system instrumentation to ensure alignment with analytic goals.
• Proactively identify and close integration or data quality gaps; champion data usability and lifecycle management.

Qualifications Technical Skills

• 6+ years of experience in data engineering, technical analytics, or platform-facing data roles.
• Advanced proficiency in Databricks, SQL, and Python. Strong knowledge of data pipeline orchestration, ETL/ELT frameworks, and data modeling.
• Hands-on experience with enterprise SaaS integrations and third-party API access.
• Demonstrated ability to build performant, governed datasets supporting analytics and ML use cases.
• Familiarity with Natural Language Processing (NLP) techniques for analyzing text-based data from collaboration tools (e.g., chat logs, survey responses).
• Experience with MLOps practices, particularly if involved in operationalizing AI models or insights.
• Execution & Strategic Impact Deep understanding of data architecture, instrumentation, and quality enforcement across complex systems.
• Ability to turn high-level business requirements into technical data products.
• Experience defining and implementing KPIs for system adoption, engagement, and behavior change.
• Skilled at delivering and scaling automated reporting solutions across teams.
• Collaboration & Influence Strong cross-functional collaboration and influencing skills, particularly with data stewards, system owners, and security/compliance.
• Excellent problem-framing and communication skills; able to provide technical leadership and connect system-level design with business impact.
• Experience leading initiatives that improve data accessibility, quality, and documentation across multiple teams.
• Preferred Familiarity with data preparation for AI/ML workflows.
• Experience building robust feedback loops using structured and unstructured input (surveys, Slack, etc.).
• Working knowledge of data governance, security, and lifecycle practices.
• Knowledge of data ethics and privacy considerations, particularly in workplace analytics and AI.
• Familiarity with a broad range of the specific collaboration/productivity tools mentioned (Microsoft 365, Google Workspace, Slack, etc.).