Senior Python Data Engineer

Senior Python Data Engineer – Data Products & Feature Engineering

Signify 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 doing

• Building 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 for

• Strong 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 experience

• AWS 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.