Senior Data Engineer

Senior Data Engineer— Data Cloud Acceleration Team About the team: We’re a fast-moving group of business-savvy technologists who value outcomes over ego. We find opportunities, deliver Version 0.1 quickly, measure real impact, and iterate. Some days that means a scrappy prototype; other days it’s a deep dive to de-noise messy data so decisions are trustworthy. What you’ll do: • Make messy data decision-ready. Profile, cleanse, reconcile, and document datasets so insights are reliable—even when the inputs are noisy. • Support new, “tip-of-the-spear” apps. Work with the application team to define the right data ingredients, stand up thin-slice pipelines, and validate results. • Move fast—carefully. Turn vague asks into testable hypotheses, build the analysis or dashboard, and ship quick iterations with attention to detail. • Guard data quality. Set up checks, audits, and alerts; track issues to closure and raise the bar on definitions, lineage, and reproducibility. • Automate the repetitive. Use SQL/Python (plus Airflow/Prefect/dbt or similar) to turn one-off analyses into lightweight, maintainable workflows. • Tell the story. Present findings with clear business context; highlight signal vs. noise, trade-offs, and recommended next steps. • Partner across functions. Collaborate with product, engineering, and client teams to ensure what we build solves real problems. • Utilize AI to improve business functions. Apart from automation, work with AI during project delivery and data mining What sets you up for success: None of these are hard gates—but they paint the picture. • Analytics craft: Advanced SQL (window functions, CTEs, performance tuning); strong exploratory analysis and sanitychecking instincts. • Self-learning and self-upgrade. Continuously learn as you go and work with the team to onboard on to new data and tools. • Data quality mindset: Experience with data testing/validation (e.g., Great Expectations, dbt tests, custom checks), metric definitions, and documentation. • Automation: Comfortable turning notebooks into repeatable jobs with Python; basic orchestration (Airflow) and CIfriendly habits. • Platforms: Familiar with cloud data warehouses/lakes (Snowflake, BigQuery, Athena, Hive, Databricks—or equivalents). • Business lens: You naturally connect analyses to KPIs, revenue, cost, and customer impact. • Communication: Sharp clarifying questions, concise write-ups, and the ability to translate between tech and business. You’ll thrive here if you… • Think pragmatically. You pick the simplest solution that gets the job done today—and improve it tomorrow. • Prototype loudly. You prefer shipping a clear v0.1 to debating a perfect v1.0. • Sweat the details. You double-check the join keys and the denominator before the demo. • Seek clarity from ambiguity. You turn fuzzy requests into crisp metrics and next steps. • Share the playbook. You document as you go so others can build on your work. 90-day markers of success • A small but solid data-quality checklist adopted on at least one key pipeline. • One or two POC intelligence applications supported with documented data ingredients, validation steps, and dashboards. • A repeatable automation that replaces a recurring manual analysis. • A short wins log showing how your work improved accuracy, speed, or business outcomes Company Summary: Zeta Global is a data-powered marketing technology company with a heritage of innovation and industry leadership. Founded in 2007 by entrepreneur David A. Steinberg and John Sculley, former CEO of Apple Inc and Pepsi-Cola, the Company combines the industry’s 3rd largest proprietary data set (2.4B+ identities) with Artificial Intelligence to unlock consumer intent, personalize experiences and help our clients drive business growth. Zeta Global is a leading AI-powered marketing technology company that enables enterprise brands to acquire, grow, and retain customers through intelligent, data-driven engagement. At ... (truncated, view full listing at source)