Data Engineer - £350PD - Remote

City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer - £350PD - Remote

Required Technical Skills

Data Pipeline & ETL

Design, build, and maintain robust ETL/ELT pipelines for structured and unstructured data

Hands-on experience with AWS Glue and AWS Step Functions

Implementation of data validation, data quality frameworks, and reconciliation checks

Strong error handling, monitoring, and retry strategies in production pipelines

Experience with incremental data processing patterns (CDC, watermarking, upserts)

AWS Data Services

Amazon S3: data lake architectures, partitioning strategies, lifecycle policies

DynamoDB: data modeling, secondary indexes, streams, and performance optimization

Amazon Redshift: foundational querying, integrations, and performance considerations

AWS Lambda for scalable data processing and orchestration

Amazon EventBridge for event-driven and decoupled data pipelines

Vector Databases & Embeddings

Strong understanding of vector database concepts, indexing strategies, and performance trade-offs

Design and implementation of embedding generation pipelines

Optimization techniques for semantic search and retrieval accuracy

Effective chunking strategies for document ingestion and processing

Experience with CockroachDB deployment and management is beneficial

Document Processing

Experience with PDF parsing libraries such as PyPDF2, pdfplumber, and AWS Textract

Integration of OCR solutions (AWS Textract, Tesseract) for scanned documents

Extraction of document structure (headings, tables, sections)

Metadata extraction, normalization, and enrichment

Handling of multiple document formats including PDF, HTML, and DOCX

Data Integration

Familiarity with SAP data structures is beneficial

Integration with PIM (Product Information Management) systems

Design and consumption of REST APIs

Programming & Querying

Python (advanced): pandas, numpy, boto3, and data processing best practices

SQL (advanced): complex queries, performance tuning, and query optimization

Data Quality & Governance

Data profiling and ongoing quality assessment

Schema validation and evolution strategies

Data lineage tracking and observability

Understanding of Master Data Management (MDM) concepts

Domain Knowledge

Product catalog data models and hierarchies

E-commerce data patterns and integrations

B2B data exchange and system integration

To apply for this role please submit your CV or contact Dillon Blackburn on (phone number removed) or at (url removed).

Tenth Revolution Group are the go-to recruiter for Data & AI roles in the UK offering more opportunities across the country than any other recruitment agency. We're the proud sponsor and supporter of SQLBits, Power Platform World Tour, and the London Fabric User Group. We are the global leaders in Data & AI recruitment

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Engineering Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data engineering in your 30s, 40s or 50s? You’re not alone. In the UK, companies of all sizes — from fintechs to government agencies, retailers to healthcare providers — are building data teams to turn vast amounts of information into insight and value. That means demand for data engineering talent remains strong, but there’s a gap between media hype and the real pathways available to mid-career professionals. This guide gives you the straight UK reality check: which data engineering roles are genuinely open to career switchers, what skills employers actually look for, how long retraining really takes and how to position your experience for success.

How to Write a Data Engineering Job Ad That Attracts the Right People

Data engineering is the backbone of modern data-driven organisations. From analytics and machine learning to business intelligence and real-time platforms, data engineers build the pipelines, platforms and infrastructure that make data usable at scale. Yet many employers struggle to attract the right data engineering candidates. Job adverts often generate high application volumes, but few applicants have the practical skills needed to build and maintain production-grade data systems. At the same time, experienced data engineers skip over adverts that feel vague, unrealistic or misaligned with real-world data engineering work. In most cases, the issue is not a shortage of talent — it is the quality and clarity of the job advert. Data engineers are pragmatic, technically rigorous and highly selective. A poorly written job ad signals immature data practices and unclear expectations. A well-written one signals strong engineering culture and serious intent. This guide explains how to write a data engineering job ad that attracts the right people, improves applicant quality and positions your organisation as a credible data employer.

Maths for Data Engineering Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data engineering jobs in the UK, maths can feel like a vague requirement hiding behind phrases like “strong analytical skills”, “performance mindset” or “ability to reason about systems”. Most of the time, hiring managers are not looking for advanced theory. They want confidence with the handful of maths topics that show up in real pipelines: Rates, units & estimation (throughput, cost, latency, storage growth) Statistics for data quality & observability (distributions, percentiles, outliers, variance) Probability for streaming, sampling & approximate results (sketches like HyperLogLog++ & the logic behind false positives) Discrete maths for DAGs, partitioning & systems thinking (graphs, complexity, hashing) Optimisation intuition for SQL plans & Spark performance (joins, shuffles, partition strategy, “what is the bottleneck”) This article is written for UK job seekers targeting roles like Data Engineer, Analytics Engineer, Platform Data Engineer, Data Warehouse Engineer, Streaming Data Engineer or DataOps Engineer.