[Remote] Staff Engineer Data

Note: The job is a remote job and is open to candidates in USA. Paylocity is an award-winning provider of cloud-based HR and payroll software solutions. The Staff Data Engineer is responsible for designing and implementing scalable data systems that support enterprise reporting and analytics, while also providing advisory input into organizational architecture decisions.

Responsibilities

• Design, build, and evolve scalable, secure, and resilient data platform capabilities that support enterprise reporting, analytics, and downstream consumers including BI and ML/AI solutions
• Develop and maintain ELT pipelines, analytical and reporting data models, and curated datasets optimized for reliability, performance, and reuse
• Write modular, well-architected, maintainable code designed for reuse across teams (shared libraries, templates, reference implementations)
• Apply scalability, reliability, and distributed-systems thinking to production data workloads (idempotency, retries, backpressure, failure isolation, recovery)
• Always think in terms of architecture for your domain and beyond; connect local decisions to platform-wide patterns and downstream consumers
• Float between teams to accelerate vague or ambiguous initiatives, reduce duplication, and increase reuse through shared capabilities and standards
• Provide advisory input into organizational and system architecture decisions that impact or integrate with the data platform
• Identify, advocate for, and complete high-impact coding projects (e.g., refactoring, simplification, platform hardening) that reduce technical debt and increase leverage
• Own and be accountable for large project features end-to-end, including design, delivery, rollout, reliability, and cost awareness
• Own health when issues arise or costs spike in areas you’re working around/with; lead triage, drive root-cause analysis, and land preventive fixes
• Proactively identify risks and issues (correctness, reliability, security, cost, delivery); communicate early and mitigate through design and execution
• Demonstrate attention to quality, performance, and observability by ensuring monitoring, actionable alerts, and practical runbooks for owned areas
• Establish, influence, and drive adoption of coding standards, data standards, and operational best practices across the data engineering space
• Contribute to and help evolve data governance and operating models that improve data quality, consistency, and trust
• Advocate for automation and efficiency across the data engineering workflow (CI/CD, testing, repeatable deployments, documentation, lineage)
• Identify and solve quality problems in others’ code with actionable review feedback, pairing, and driving follow-through to resolution
• Maintain a high bar in code reviews and technical design discussions (clarity, correctness, testing, operability, alignment to standards)
• Balance technical complexity with user experience and consumer needs (reporting applications, BI consumers, ML/AI use cases)
• Gain alignment from team-level to org-wide on technical strategy and best practices by influencing through clear proposals, prototypes, and coaching
• Effectively partner with project stakeholders and leadership to clarify goals, manage tradeoffs, and drive outcomes
• Serve as a formal technical lead on projects; delegate work effectively by providing context and success criteria, fostering growth and ownership throughout the team
• Focus on high-priority projects first; explicitly deprioritize lower-value work and communicate tradeoffs clearly
• Actively mentor or coach all team members, providing feedback to grow hard skills (design, SQL/Python quality) and soft skills (communication, influence)
• Resolve conflict and foster productive discussions; encourage an open and inclusive culture
• Drive continuous team improvement by turning learnings into updated standards, patterns, and practices
• Demonstrate significant learning outside of primary team responsibilities and apply it to improve the data platform and team effectiveness

Skills

• Bachelor's degree in Computer Science, Data Engineering, or related discipline or equivalent practical experience
• 7+ years of experience in data engineering, including ownership of large-scale production data systems and hands-on leadership as a technical lead
• Strong expertise in cloud architectures, distributed systems, and automation, especially within AWS-based data platforms
• Deep hands-on experience with reporting and analytics data modeling and ELT pipelines
• Strong experience with Snowflake, dbt, Python, and SQL with exception-level proficiency
• Experience with real-time or event-driven data processing (e.g., Kinesis, EventBridge) and orchestration (e.g., MWAA/Airflow)
• Experience enabling a broad set of consumers, from reporting applications and BI to ML/AI solutions built on top of curated data products
• Proven track record optimizing large-scale data platforms for reliability, performance, and cost

Benefits

• Medical
• Dental
• Vision
• Life
• Disability
• 401(k) match
• Perks that support you, your family, and your finances

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