What are we looking for We're always looking for the next generation of SOLVERS who want to make a difference. We're not looking for someone who just drags and drops in a BI tool. We're looking for a data artisan - someone who builds elegant pipelines, loves a good DAG, and dreams in schema. At NxtGenAI, data isn't just part of our platform - it is the platform. Qualities that SHINE at NxtGen Analytics Pipeline perfectionist You get a little twitchy when you see an unpartitioned table or a broken DAG. Clean, efficient, and bulletproof pipelines are your love language. Data whisperer You speak fluent SQL, translate business needs into data models, and can sniff out a missing join like a bloodhound. Cloud native by instinct You believe that storage buckets should never overflow, jobs should be serverless when they can be, and Airflow is best enjoyed with auto-retries. Job Responsibilities 1. Design and build scalable, secure, and efficient data pipelines on cloud platforms (preferably GCP, but we're cloud-agnostic at heart). 2. Develop ETL/ELT workflows using tools like Dataflow, Apache Beam, dbt, or Airflow. 3. Collaborate with AI engineers and product teams to prepare and optimize data for machine learning and reporting workflows. 4. Model raw data into clean, accessible structures (think: lakehouses, not swamp castles). 5. Implement robust data quality checks, monitoring, and lineage tracking. 6. Work with structured and semi-structured data (JSON, Parquet, Avro, etc.) from various sources - because we love a good data mystery. 7. Optimize queries, storage costs, and performance across BigQuery, Snowflake, or equivalent cloud-native warehouses. 8. Support data governance, security, and compliance practices - especially when handling sustainability or ESG-critical datasets. 9. Document processes clearly so your future self (and teammates) don't curse your name. Requirements 3-7 years of experience in data engineering, data warehousing, or analytics engineering. Solid experience with cloud-native data stack - GCP (BigQuery, Dataflow, Pub/Sub), AWS (Redshift, Glue), or Azure (Data Factory, Synapse). Proficiency in SQL, Python, and orchestration tools (Airflow, Prefect, dbt). Familiarity with CI/CD workflows and Git-based versioning. Strong understanding of data modeling, warehousing principles, and performance tuning. Bonus points for exposure to ESG data models, sustainability metrics, or regulatory frameworks like IFRS S1/S2. Curiosity, pragmatism, and a genuine love for clean data and clean code. Show more Show less