Sr. Data Engineer / Scientist - Data Quality & Insights Team
Senior data engineer/scientist answering business questions for internal stakeholders and customers: building and deploying ML models in Databricks (PySpark, MLflow), running statistical/causal analyses, architecting data-quality frameworks and store migrations, and presenting results in Power BI. Core stack: Python, SQL Server/T-SQL, Databricks, PostgreSQL.
- Compensation
- Not disclosed
- Location
- Remote
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 3+ years applying statistics and machine learning to business problems (regression, classification, clustering, time-series, correlation/causal analysis, hypothesis testing, anomaly detection)
- Hands-on experience building and deploying ML and analytics workloads in Databricks or comparable lakehouse (PySpark, Spark SQL, Delta Lake, Jobs/Workflows)
- Strong Python for data science (pandas, scikit-learn, statsmodels, or equivalent) alongside SQL
- 5+ years SQL and database development, including 3+ years with T-SQL and Microsoft SQL Server
- Advanced Power BI development and architecture experience
- Track record partnering with business teams (Customer Success, Sales, Marketing, Product, executives) to define questions and drive decisions
- Excellent written and verbal communication, including explaining statistical results to non-technical audiences
- Strong understanding of ETL/ELT architectures at scale, including lakehouse patterns
- Strong scripting and automation framework development skills
- Experience managing complex production deployments
- Comfort with after-hours work and leading on-call efforts
Nice to have
- Experience with CRM systems and automotive dealership data, or knowledge of multiple CRM providers
- Advanced Databricks: Unity Catalog governance, Delta Lake optimization, MLflow model registry
- MLOps and feature store implementation
- Experiment design (A/B testing, uplift measurement) and causal inference methods
- Experience presenting to or working directly with external customers on data projects
- Experience with PostgreSQL or other database platforms
- Knowledge of AWS services (EC2, RDS, S3, CloudWatch, EKS)
- Knowledge of C# or other scripting languages
- Experience with modern data stack technologies (Snowflake, dbt)
- Understanding of data governance and compliance requirements
- Performance tuning at enterprise scale
- Streaming technologies (Kafka, Spark Streaming)
- Project management certifications (PMP, Agile, Scrum)
- Experience with Twilio or other CPaaS; familiarity with telecom regulatory requirements (A2P/10DLC, TCPA, STIR/SHAKEN)
- Experience writing APIs consumed by third parties
- Experience with speech-to-text or building product features on top of LLM/ML output
Full description
A Senior Data Engineer / Scientist on the Data Quality & Insights team answers business questions for internal stakeholders and customers. The role pairs applied statistics and machine learning with hands-on data engineering in SQL Server, Databricks, and PostgreSQL: building and deploying models, running correlation and causal analyses, detecting anomalies in customer and transaction data, and presenting the results in Power BI, Databricks, and or in-house built software tools, and in conversation with the people who asked.
Internally, the team supports Customer Success, Sales, Marketing, Product, and the executive team. Understanding the question behind a request, choosing the analysis that actually answers it, and explaining the result to a non-technical audience matter as much as the modeling itself.
The role also owns the delivery of technical projects such as store merges and large data migrations, mentors engineers on the team, and sets the standard for how analysis moves from a notebook to a production deliverable.
What You'll DoAnalytics and data science
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Build, validate, and deploy machine learning models in Databricks (PySpark, Spark SQL, Delta Lake, MLflow) for forecasting, scoring, segmentation, and anomaly detection
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Run statistical analyses, including correlation, regression, hypothesis testing, cohort analysis, and time-series analysis, and state plainly what the data does and does not support
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Architect data quality frameworks in Databricks using profiling, rule-based validation, and statistical anomaly detection, and define how they move from notebooks to production jobs
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Build the pipelines and feature sets that analytics and ML workloads depend on
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Design advanced Power BI models and reports that make findings usable by people who will never open a notebook
Business partnership
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Serve as the analytical partner to Customer Success, Sales, Marketing, Product, and the executive team: understand the question behind the request, scope the analysis, and deliver a clear answer
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Present findings to technical and non-technical audiences, including executive readouts
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Turn recurring requests into self-service reporting so the team spends its time on new questions
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Build the capacity to work directly with customers on custom data projects as that need develops
Data platform and quality
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Lead complex store merges and architect store migration strategies