Senior Data Analyst

London Approach; Kentucky; On-site; USD 75,000 - 95,000; Full time

Job summary

London Approach is seeking a Data Analyst / Analytics Engineer to transform complex data into actionable insights. This role focuses on analyzing large datasets, identifying trends, and improving reporting workflows through SQL and Python.

The ideal candidate has 3–6 years of experience, strong analytical skills, and familiarity with AI tools to enhance data analysis. You will work closely with business stakeholders and contribute to better decision-making through clear communication of findings.

Qualifications

• 3-6 years of experience in a data-focused role.
• Ability to work with messy data and validate findings.
• Knowledge of statistical fundamentals.

Responsibilities

• Analyze complex datasets to identify trends.
• Partner with stakeholders to define key questions.
• Write SQL queries for reporting and analysis.

Skills

Advanced SQL skills

Strong Python skills

Experience with AI tools

Statistical analysis

Strong communication skills

Tools

pandas

NumPy

dbt

Airflow

Job description

We are seeking a Data Analyst / Analytics Engineer to help turn complex data into clear, actionable business insights. This role is ideal for someone who is highly analytical, technically strong, and comfortable working across SQL, Python, modern data platforms, and AI-enabled analytics tools.

The right candidate will not just respond to reporting requests, but will proactively identify trends, inconsistencies, risks, and opportunities within the data. This person should be able to work with business stakeholders, translate questions into analysis, and communicate findings in a way that helps leaders make better decisions.

This role is a strong fit for someone who enjoys working hands-on with data, improving reporting workflows, using AI tools to move faster, and helping build smarter, more scalable analytics processes.

Responsibilities

• Analyze large and complex datasets to identify trends, patterns, anomalies, risks, and business opportunities.
• Partner with business stakeholders to understand reporting needs, define key questions, and translate business problems into data-driven analysis.
• Write advanced SQL queries using joins, CTEs, window functions, and reusable logic to support reporting, dashboards, and ad hoc analysis.
• Use Python to clean, transform, analyze, and visualize data using tools such as pandas, NumPy, and related libraries.
• Move analysis from exploratory notebooks into repeatable scripts, automated workflows, or production-ready processes.
• Build and maintain reports, dashboards, and analytical datasets that support business decision-making.
• Use AI and LLM tools to accelerate analysis, automate reporting, improve research workflows, and create more efficient analytics processes.
• Proactively investigate data quality issues, inconsistencies, and unexpected results to ensure accurate insights.
• Conduct statistical analysis, including hypothesis testing, regression, cohort analysis, and other methods to evaluate business performance.
• Communicate findings clearly through written summaries, presentations, and recommendations tailored to both technical and non-technical audiences.
• Work cross-functionally with data, product, operations, finance, marketing, engineering, or leadership teams depending on business needs.
• Help improve data processes, documentation, reporting standards, and analytics best practices.

Required Qualifications

• 3–6 years of experience in a Data Analyst, Analytics Engineer, Business Intelligence Analyst, or similar data-focused role.
• Advanced SQL skills, including complex joins, CTEs, window functions, query optimization, and reusable query design.
• Strong Python skills, including hands-on experience with pandas, NumPy, data visualization, and scripting.
• Experience working with imperfect, incomplete, or messy data and knowing how to validate results before presenting findings.
• Practical experience using AI, LLM, or generative AI tools to improve analysis, automate reporting, or build smarter workflows.
• Proven ability to identify insights beyond the original request and proactively surface trends, risks, or opportunities.
• Strong written and verbal communication skills, with the ability to explain complex analysis in a clear, business-focused way.
• Solid understanding of statistical fundamentals, including hypothesis testing, regression, cohort analysis, and correlation versus causation.
• Ability to work independently, manage multiple priorities, and collaborate with technical and non-technical stakeholders.
• Strong attention to detail and a high degree of ownership over data accuracy and analytical quality.

Preferred Qualifications

• Experience with dbt, Airflow, or similar data transformation and workflow orchestration tools.
• Familiarity with cloud data warehouses such as BigQuery, Snowflake, Redshift, or Databricks.
• Hands-on experience calling LLM APIs or building AI-assisted reporting, analytics, or automation workflows.
• Exposure to machine learning concepts such as feature engineering, model evaluation, or working alongside data scientists.
• Experience with RAG pipelines, embeddings, vector search, or other AI/data retrieval techniques for analytics use cases.
• Experience building executive-facing dashboards, recurring reports, or self-service analytics tools.
• Familiarity with BI or visualization platforms such as Tableau, Power BI, Looker, Mode, or similar tools.
• Experience supporting product, finance, marketing, operations, customer, or growth analytics.

Ideal Candidate

The ideal candidate is curious, technically capable, and business-minded. They are comfortable writing advanced SQL, using Python to solve data problems, and applying AI tools to make analytics faster and more effective. They know how to challenge assumptions, validate their work, and explain findings in a way that drives action.

This person should be proactive rather than purely reactive, someone who can spot when the data does not make sense, ask better questions, and uncover insights that were not obvious at the start of the request.