Senior Data Engineer

Accylerate; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

Accylerate is seeking a Senior Data Engineer to lead data quality engineering, monitoring, and remediation across pipelines. You will design validation rules, automate checks, and partner with engineers and analysts to ensure data accuracy, completeness, and timeliness for critical reports.

You will apply SQL, Python, and cloud data tools to analyze large datasets, tackle complex data issues, and drive data governance and observability initiatives across teams.

Qualifications

• BS degree in Engineering, Computer Science, or related field / equivalent experience.
• 10+ years of experience in quality testing / data quality engineering.
• Strong SQL skills and experience writing complex queries across multiple systems.
• Experience in data engineering, analytics engineering, data quality, software engineering, or related field.
• Exposure to AI-assisted development tools (e.g., GitHub Copilot, Claude).
• Experience with cloud data platforms and tools (AWS, Redshift, Athena, Snowflake, Databricks).
• Proficiency in Python or TypeScript for automation and data analysis.
• Experience designing or maintaining data quality checks and monitoring.
• Strong understanding of data structures, modeling, transformations, and data lineage.
• Ability to translate complex problems into structured analysis and actions.
• Familiarity with BI tools like Tableau or QuickSight is a plus.
• Strong communication and collaboration across technical and non-technical teams.

Responsibilities

• Design and implement automated data quality checks across datasets and pipelines.
• Build monitoring, alerting, and observability for data pipelines to detect anomalies and drift.
• Develop reconciliation processes across source systems and reports.
• Collaborate with engineers and analysts to define quality rules and validation criteria.
• Create reusable tooling for profiling, testing, and validating data in production and non-production environments.
• Investigate data issues by tracing data across systems and validating business logic.
• Support incident response for data-related production problems, especially during critical periods.
• Remediate defects with cross-functional teams to improve upstream processes.
• Communicate findings and remediation steps clearly to technical and non-technical stakeholders.

Skills

SQL proficiency

Data quality mindset

Data investigation

Python

Cloud data platforms

BI/Reporting tools

Documentation & communication

Leadership & collaboration

Education

BS degree in Engineering, Computer Science, or related field / equivalent experience

Tools

GitHub Copilot

Claude

AWS

Redshift

Athena

Snowflake

Databricks

Job description

Ideal Candidate Profile:

Seeking a Senior Data Engineer with strong experience in writing super complex SQL queries to extract data for reports. Candidate should be capable of handling, consuming and extracting large sets of data for analysis and reports and should have experience with materialized views and performance tuning. As a Senior Data Engineer, you’ll help ensure that the data powering our products, reporting, and operational workflows is accurate, complete, timely, and trustworthy. You’ll design and implement data quality checks, monitoring, and remediation processes across pipelines and platforms, working closely with engineers, analysts, product teams, and business stakeholders. Your work will help identify issues early, reduce operational risk, and improve confidence in the data used to support critical decisions and digital assessment experiences. This role blends hands‑on technical investigation, automation, and cross‑functional collaboration.

Job Duties & Responsibilities

Data Quality Engineering & Monitoring (45%)

• Design and implement automated data quality checks for completeness, accuracy, consistency, freshness, and schema integrity across critical datasets and pipelines.
• Build monitoring, alerting, and observability solutions to detect anomalies, pipeline failures, data drift, and unexpected changes before they impact downstream consumers.
• Develop and maintain reconciliation processes across source systems, transformed datasets, reports, and operational outputs.
• Partner with engineers and analysts to define quality rules, acceptance criteria, and data validation requirements for new and existing systems.
• Create reusable frameworks, scripts, and tooling for profiling, testing, and validating data in production and non‑production environments.

Investigation, Analysis, & Remediation (35%)

• Investigate data issues by tracing data across systems, transformations, and business workflows to identify root causes and recommend fixes.
• Use SQL, Python, and cloud data tools to analyze large datasets, isolate anomalies, and validate business logic.
• Support incident response and issue resolution for data‑related production problems, especially during high‑priority operational periods.
• Work with cross‑functional teams to remediate defects, improve upstream processes, and reduce recurrence of common data issues.
• Communicate findings clearly to both technical and non‑technical stakeholders, including issue summaries, remediation recommendations, and quality trends.

Governance, Documentation, & Team Success (20%)

• Document data definitions, validation logic, lineage, quality rules, and remediation procedures to improve transparency and operational readiness.
• Contribute to best practices for testing, version control, deployment, and ongoing maintenance of data quality solutions.
• Participate in Agile ceremonies, code reviews, and team planning, helping break work into manageable tasks and improve team productivity.
• Support the development of standards for data governance, ownership, and operational excellence across the team.
• Partner with stakeholders to improve trust in shared data assets and ensure quality considerations are built into delivery from the start.

Required Skills & Experience

• BS degree in Engineering, Computer Science, or related field / equivalent experience
• 10+ years of general experience in quality testing
• Strong SQL skills and experience writing complex queries to analyze, validate, and troubleshoot data across multiple systems.
• Professional experience in data engineering, analytics engineering, data quality, software Engineering, or a related field with a strong focus on data investigation and validation.
• Exposure to AI-assisted development tools (e.g., GitHub Copilot, Claude) and hands‑on experience applying to build and deploy AI agents that automate data pipelines, write code and testing workflows.
• Experience working with cloud data platforms and tools such as AWS, Redshift, Athena, Snowflake, Databricks, or similar technologies.
• Proficiency in Python or type script language used for automation, testing, and data analysis.
• Experience designing or maintaining data quality checks, monitoring, alerting, or observability processes for production datasets or pipelines.
• Strong understanding of data structures, data modeling, transformations, lineage, and common sources of data defects.
• Ability to investigate issues across systems, apply business logic, and translate ambiguous problems into structured analysis and action.
• Experience working with BI/reporting tools such as Tableau, QuickSight, or similar platforms is helpful.
• Strong communication, documentation, and collaboration skills, with the ability to work effectively across technical and non‑technical teams.
• A learner's mindset, curiosity about emerging technologies and AI-enabled tools, and a drive to improve systems and processes continuously.
• Ability to support high‑priority operational periods and respond effectively to production data issues when needed.
• Strong interpersonal and consultative skills.
• Highly self‑motivated and directed, with keen attention to detail.
• Strong leadership skills and customer satisfaction orientation.