Mids-Level Data Engineer

Data Engineer role

Core experience

• 3–5 years' professional experience in Data Engineering, data-focused backend engineering or data architecture.
• Proven commercial experience actually building, maintaining and troubleshooting production ETL/ELT pipelines rather than purely academic/project exposure.
• Experience working with data warehouses, data architecture, data modelling and production data environments.
• Bachelor's degree in Computer Science, Software Engineering, Data Engineering, Information Systems or related field,
  or equivalent practical experience
  .

Python & SQL — essential

• Strong/advanced Python for data engineering, ideally including Pandas and Py Spark ;
  FastAPI exposure is also specified at mid-level.
• Strong to expert SQL
  , including complex joins, window functions, debugging, query optimisation and query-plan/performance optimisation.
• Must be capable of troubleshooting and optimising both SQL queries and Python data jobs
  .
• This is a significant upgrade from the original requirement, where only good SQL and basic programming/scripting were required.

ETL/ELT & pipelines — essential

• Design, build, test, maintain and optimise scalable ETL/ELT pipelines
  .
• Batch processing experience, with real-time/streaming exposure highly valuable.
• Data ingestion from multiple sources including:
  • REST APIs
  • PostgreSQL/MySQL or other relational databases
  • Third-party/SaaS platforms
  • Structured, semi-structured and unstructured data
  • Ideally Kafka/RabbitMQ or other message queues.
• Production pipeline monitoring, error handling, debugging, incident/root-cause resolution and preventative improvements.

Data warehousing & modelling — essential

• Hands-on experience with modern data warehouse/lakehouse platforms such as Snowflake, Big Query, Amazon Redshift or Databricks
  .
• Strong understanding of data models, schemas and dimensional modelling.
• Practical exposure to Kimball/star schema
  ;
  Data Vault is advantageous.
• Understanding of storage/query optimisation including indexing, partitioning and compression.

Cloud — essential for the upgraded role

• Solid working knowledge of at least one major cloud platform:
  AWS, Azure or GCP
  .
• Hands-on exposure to the platform's data services rather than simply having a cloud certification.
• The junior specification only required familiarity with cloud platforms such as Fabric, AWS or Big Query; the upgraded role requires solid working knowledge.

Orchestration & modern data stack — essential

• Commercial experience with Apache Airflow, dbt, Prefect or a comparable orchestration/workflow framework
  .
• Experience transforming data using dbt, Spark or cloud-native warehouse tools
  .
• Understanding of modern scalable data architecture.

Git & CI/CD

• Strong Git experience.
• Git Hub/Git Lab workflows.
• Basic CI/CD pipeline automation for deploying data code.
• Should be accustomed to collaborative development, code reviews and version-controlled production code.

Data quality, governance & security

• Automated data validation/testing, preferably dbt tests, Great Expectations or equivalent
  .
• Data quality monitoring and alerting.
• Data lineage, metadata and data dictionaries.
• Understanding of access controls, masking and encryption.
• Awareness of data governance/compliance requirements.

AI/ML data engineering

• Must understand how Data Engineering supports AI/ML workloads
  .
• Preparing clean, reproducible datasets for model training, evaluation and inference.
• Data preprocessing and validation for ML pipelines.
• Exposure to feature stores, MLOps and generative-AI integrations is highly advantageous.
• The upgraded requirements build directly on the newer Junior specification, which already introduced structured/unstructured data preparation, vector databases and RAG pipelines
  .
• Bonus technologies include MLflow, Feast, Vertex AI and Sage Maker
  .

Highly advantageous / differentiators

• Kafka, AWS Kinesis or Apache Flink
• Docker
• Kubernetes
• Terraform or Cloud Formation
• No
  
  SQL — MongoDB, DynamoDB or Redis
• Spark/Py Spark
• Real-time streaming pipelines
• MLOps
• Cost optimisation within cloud data environments.

3–5 years relevant commercial experience + strong Python + advanced SQL + hands-on ETL/ELT pipeline development + data warehousing / modelling + AWS / Azure / GCP + Airflow / dbt / Prefect or equivalent + Git + genuine production troubleshooting / optimisation experience, production Data Engineering experience.

additional advantage
Snowflake / Databricks / Big Query / Redshift, PySpark/ Spark, CI/CD, automated data testing, AI/ML data pipelines and ideally Kafka/streaming
.