Associate Data Analyst

impact.com is the world’s leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates, impact.com empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products—Performance (affiliate), Creator (influencer), and Advocate (customer referral)—unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust, impact.com helps brands show up where it matters most. Today, over 5,000 global brands, including Walmart, Uber, Shopify, Lenovo, L’Oréal, and Fanatics, rely on impact.com to power more than 225,000 partnerships that deliver measurable business results.

The Associate Data Analyst is responsible for collecting, analyzing and interpreting large and complex datasets to identify patterns and trends, surfacing facts and assist in developing insights and providing data-driven outputs and recommendations to support business decisions. The ideal candidate will have a keen interest in and some prior experience of data analysis, reporting and forecasting. The associate data analyst needs to be a clear communicator that can confidently interact with technical and non-technical stakeholders.

The Associate Data Analyst will form part of and actively participate in the data analytics competency, with special focus on building data and reporting outputs as directed by senior team members. The analyst will be a contributing member of the analytics center of excellence (ACoE) and adopt best practices as outlined by ACoE leadership.

• →Extract and analyze data from various sources including relational databases (mySQL, postgres), big data technologies (BigQuery, Kudu, Impala, SingleStore, Hive), file sources (parquet, ORC, avro, csv, xlsx) and reporting systems (Looker, DOMO).
• →Assist with the identification, creation and maintenance of master datasets.
• →Participate in data stewardship initiatives.
• →Clean and manipulate data to ensure high quality and integrity of reporting outputs.
• →Contribute to technical specifications for new data assets (marts, tables, views, cubes).
• →Analyze source systems and help identify business logic embedded in the underlying datasets.
• →Use statistical techniques to identify trends and derive patterns and insights embedded in the data.
• →Identify and communicate data quality and data validation issues in productionalized datasets.
• →Create visualizations and dashboards as directed by the squad lead and required by business stakeholders.
• →Build and maintain dashboards and reports to support business stakeholders.
• →Perform ad-hoc data analysis to determine suitability of a data source for use in data marts and reporting outputs or to assess key performance indicators (KPIs).
• →Communicate findings and recommendations to both technical and non-technical team members in a clear and concise manner.

• Education: Bachelor's degree in Mathematics, Statistics, Economics, Computer Science, or a related field.
• Experience: 2+ years of experience in a data analysis, business intelligence, or data engineering role.
• Core Technical Skills: * Strong Python and SQL coding skills.
• Demonstrable data analysis and data visualization skills.
• Hands-on exposure to data wrangling (data cleaning, manipulation, and preparation).
• Tools & Platforms: * Exposure to the Google Cloud Platform (GCP) and its integrated technologies.
• Familiarity with data visualization tools such as Tableau, Power BI, Looker, SSRS/SSAS, or QlikView.
• Professional & Soft Skills:
• Strong analytical and problem-solving skills.
• Excellent verbal and written communication skills.
• Ability to work independently and collaboratively in a fast-paced, dynamic environment.

• Advanced Data Stack: Deep exposure to DataBricks, PySpark, and BigQuery.
• Data Systems: Exposure to NoSQL data systems and streaming platforms (HBase, Redis, Kafka).
• Data Science: Exposure to machine learning libraries, tools, and protocols (both supervised & unsupervised learning).
• Statistical Depth: Exposure to standard statistical modeling and forecasting methodologies.
• Domain Expertise: Prior experience working within the Marketing Technology (MarTech) industry.