Corporate Houston N67 Data Analyst

We are seeking Data Analyst to support strategic and operational decision-making through rigorous data analysis, automation, reporting, and visualization.  The ideal candidate has a combination of strong analytical and SQL skills with fluency in data modeling and automation.  Responsibilities include:

  • Design, build, and maintain automated data pipelines and dashboards to support business operations, planning, and performance tracking.
  • Conduct data validation, cleansing, and transformation on structured and semi-structured datasets using SQL, Python, and Spark.
  • Perform exploratory data analysis and develop ad-hoc analytical models to identify trends, anomalies, and optimization opportunities.
  • Support data governance, quality assurance, and contract validation efforts within the enterprise data platform.
  • Develop and maintain KPI frameworks across finance, supply chain, and operations domains; ensure data definitions are consistent across systems.
  • Translate complex data into clear, actionable insights through well-structured narratives, visualizations, and business presentations.
  • Partner closely with engineering, finance, operations, and product teams to align analytical outputs with business priorities.
  • Contribute to the development of analytical feature stores and assist Data Science teams with dataset preparation for modeling and ML pipelines.
  • Expertise in SQL and Python, with hands-on experience using Pandas and PySpark for analytical workflows.
  • Deep understanding of data modeling, ETL design, and performance optimization for analytical queries.
  • Experience in business analytics, forecasting, or operational planning within enterprise environments preferred.
  • Familiarity with EKS and EMR deployments, as well as data frameworks such as Apache Airflow, Flink, and Kafka, is a plus.
  • Strong communication skills, with the ability to articulate analytical findings and recommendations to technical and non-technical stakeholders alike.
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When you bring additional fields into a conversion, Quickbase often finds inconsistencies. For example, say you're converting your Companies column into its own table. One company, Acme Corporation, has offices in New York, Dallas and Portland. So, when you add the City column to the conversion, Quickbase finds three different locations for Acme. A single value in the column you're converting can only match one value in any additional field. Quickbase needs you to clean up the extra cities before it can create your new table. To do so, you have one of two choices:

  • If you want to create three separate Acme records (Acme-New York, Acme-Dallas and Acme-Portland) click the Conform link at the top of the column.
  • If the dissimilar entries are mistakes (say Acme only has one office in New York and the other locations are data-entry errors) go back into your table and correct the inconsistencies—in this case, changing all locations to New York. Then try the conversion again.

Read more about converting a column into a table.