Senior Python Data Engineer

Senior Python Data Engineer – Data Products & Feature EngineeringSignify Technology is partnering with a software business looking for a Senior Python Data Engineer to turn complex, multi-source data into reliable data products that support analytics, AI and machine learning.This is a hands-on role for someone who enjoys solving messy data problems, building production pipelines and creating datasets that other teams can trust.What you’ll be doingBuilding Python pipelines to ingest, clean and transform data from APIs, databases, cloud storage and third-party platforms.Combining structured and unstructured sources, including events, transactions, documents, logs and text.Designing schemas and entity-resolution logic to connect related records across systems.Creating ML-ready datasets and features for classification, ranking, anomaly detection and predictive workflows.Building checks for missing data, duplicates, stale sources and schema changes.Preparing content for AI retrieval and search through cleaning, chunking, indexing and validation.Working closely with product, engineering and AI/ML teams to translate business needs into production-ready data products.What we’re looking forStrong Python engineering experience building production data pipelines or backend data systems.Strong SQL skills and experience with relational databases, data warehouses or lakehouse architectures.Experience building ETL/ELT pipelines using complex, multi-source data.A track record of cleaning, normalising, deduplicating and enriching data.Experience creating analytical feature sets, ML-ready datasets or feature engineering pipelines.Confidence working with unstructured text and third-party integrations.Experience with AWS, GCP or Azure.A strong approach to data quality, monitoring, traceability and maintainability.Useful additional experienceAWS tools such as S3, Lambda, Glue, Step Functions, Athena or Redshift.Embeddings, vector databases, semantic search or RAG pipelines.Web scraping and crawling tools such as Playwright, BeautifulSoup or Scrapy.Feature stores, identity mapping or customer/account data modelling.SaaS, CRM, support or product usage data.You don’t need to be a data scientist or ML researcher. You do need to understand how to build reliable data foundations that make analytics, AI and ML systems work in production.