Senior Software Data Engineer

NetApp; München; Vor Ort; EUR 80.000 - 100.000; Vollzeit

Zusammenfassung

A global cloud storage company is seeking a Senior Software Data Engineer to tackle complex problems and deliver scalable solutions. This role will focus on defining and designing data pipelines, driving insights through analytics, and leveraging AI/ML for innovative capabilities. The ideal candidate has a strong background in data engineering, expert-level SQL and Python skills, along with experience in developing production-grade pipelines and dashboards. This is an exciting opportunity for someone looking to lead data initiatives in a dynamic environment.

Qualifikationen

• 8+ years in data engineering/analytics with demonstrable impact.
• Hands-on with orchestration, CI/CD for data, and data testing/observability.
• Detail-oriented with a focus on data accuracy and quality.

Aufgaben

• Own complex business problems end-to-end: define, design, deliver.
• Design and lead implementation of error-free ELT/ETL pipelines.
• Analyze large datasets to diagnose root causes and deliver insights.
• Partner with MLEs to productionize models with AI/ML capabilities.

Kenntnisse

Expert SQL

Python

Snowflake

dbt for ELT modeling

Tableau

Power BI

Data governance

Orchestration (Airflow/Prefect)

Statistical modeling techniques

Ausbildung

Bachelor's degree in computer science, mathematics, statistics, or related field

Tools

Airflow

Prefect

Jobbeschreibung

Title: Senior Software Data Engineer

Location: Bangalore, Karnataka, IN

Job Summary

NetApp’s Cloud Storage Business Unit is seeking a highly skilled Software Data Engineer. In this senior role, you will own complex business problems end-to-end: define what needs to be solved, design the approach, and deliver scalable solutions. You will shape the analytics and data platform strategy for key Cloud Storage outcomes, set technical direction, and build AI/ML-powered capabilities that drive impact across products and functions. This includes architecting durable data foundations (Snowflake + dbt), elevating data quality standards, and transforming signals from customer lifecycle, product telemetry, GTM, and finance into actionable insights and measurable business results.

Business & Outcomes

• Develop a deep understanding of Cloud Storage metrics, processes, funnels, and customer lifecycles; articulate the “why” behind changes in ARR, adoption, expansion / NRR, support cost, and efficiency.
• Define problem statements and OKRs / KPIs; align roadmaps with BU priorities and partner teams (Product, Engineering, Cloud Ops, Sales, Finance).

Data Engineering at Scale

• Design and lead implementation of error‑free ELT / ETL pipelines into the data lake / warehouse (e.g., Snowflake + dbt), starting with PoCs that prove business value prior to scale‑out; productionize with orchestration (Airflow / Prefect) and CI / CD.
• Establish gold‑standard data models and contracts for product telemetry, billing, and GTM datasets; enforce versioned schemas, SLAs / SLOs, lineage, and observability.

Insight Generation & Decision Support

• Analyze large, complex datasets to diagnose root causes, opportunities, and risk; deliver prescriptive recommendations to PMs and business leaders, not just descriptive dashboards.
• Build and maintain executive‑grade dashboards and self‑serve semantic layers (Tableau / Power BI) with clear narrative storytelling.

AI / ML Innovation

• Leverage modern AI / ML (forecasting, uplift modeling, causal inference, anomaly detection, LLM‑assisted analytics) to automate insights and power new capabilities, e.g., churn / expansion prediction, price‑performance guidance, intelligent cost / efficiency recommendations.
• Partner with MLEs to productionize models with feature stores, monitoring, governance, and responsible‑AI practices.

Data Quality, Governance & Security

• Design and enforce robust data quality checks (tests, expectations, anomaly rules) and steward data governance (access, PII handling, auditability) across different data pipelines.

Technical Leadership & Influence

• Serve as tech lead for cross‑functional analytics initiatives; mentor IC3–IC4 engineers and establish standards for modeling, testing, documentation, and review.

Education

• Bachelor’s degree in computer science, mathematics, statistics, or related field.
• 8+ years in data engineering / analytics with demonstrable impact on product or business outcomes.
• Expert SQL and Python; strong command of Snowflake and dbt for ELT modeling; experience building production‑grade pipelines and data contracts.
• Proven track record delivering dashboards / visualizations (Tableau / Power BI) that drive action.
• Hands‑on with orchestration, CI / CD for data, and data testing / observability.
• Ability to translate technical concepts for non‑technical audiences and influence senior stakeholders across time zones.
• Excellent communication and collaboration skills, with the ability to work effectively with stakeholders at all levels.
• Detail‑oriented with a focus on data accuracy and quality.
• Expertise with statistical modeling techniques.