Senior Data Analyst (Graph focused), Fixed Term

About the role

This fixed-term engagement supports data integrity for a knowledge graph by validating datasets and query outputs across storage technologies.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Systematic validation of loaded datasets confirms completeness, accuracy, and structural integrity against source data, ensuring trustworthiness for the team.

Business unit compliance use cases are collected and documented to align validation activities with real operational requirements.

Outliers in benchmark result distributions are identified, variance across cold/warm/concurrent runs is assessed, and flagged results are highlighted for deeper investigation before scoring, protecting downstream decisions.

Summary statistics and data quality reports are produced to inform architecture assessments and support transparent decision-making.

Requirements

Large, complex datasets are validated by identifying discrepancies, tracing root causes, and documenting findings clearly based on demonstrated experience.

Analytical queries are written against relational databases with PostgreSQL experience preferred, reflecting strong SQL skills.

Data at significant scale - hundreds of millions of records - is worked with where manual spot-checking is insufficient and systematic validation approaches are required.

ETL pipelines and typical data quality issues arising during loading and transformation are understood through familiarity.

Statistical methods are applied to data quality assessment, including distribution analysis, outlier detection, variance analysis, and sampling validation.

Meaningful performance differences are distinguished from noise by interpreting benchmark result data.

Multiple database technologies and query languages are worked with, including PostgreSQL, graph databases, and Databricks as part of normal validation work.

Databricks or similar distributed data platforms (Spark, Delta Lake) experience is present.

Clear communication is demonstrated in validation findings, defect reports, and root cause analyses for engineers and product stakeholders.

Independent work is conducted under minimal supervision, taking direction from peers without requiring structured management oversight.

Cross-functional engineering team experience is required.

Practical notes

DEPT®® is an equal opportunity employer.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

The role centers on data integrity and validation for graph and related technologies within a tech and marketing services environment. Common tools include SQL, graph databases, and distributed data platforms such as Databricks. Statistical methods and clear documentation guide engineering decisions.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.