Design, develop, and optimize enterprise-scale Snowflake data warehouses, pipelines, models, queries, and workflows. Build ingestion and transformation processes with Python, PySpark, Airflow, and AWS. Ensure data quality, governance, security, compliance, performance, and cost efficiency. Automate deployment and monitoring through CI/CD and DevOps practices, troubleshoot production issues, and collaborate with stakeholders and engineering teams. Provide technical leadership, lead discussions, and mentor engineers.
Role Summary
We are seeking a highly skilled Senior Data Engineer with 8–15 years of experience in designing, developing, and implementing enterprise-scale data solutions. The ideal candidate will possess strong expertise in Snowflakeand hands-on experience with Python, PySpark, AWS, and Airflow for building scalable, high-performance data platforms. The role demands strong technical leadership, problem-solving capabilities, and the ability to collaborate effectively with cross-functional teams to deliver robust data engineering solutions.
Key Responsibilities
- Design, develop, and optimize data pipelines and data models using Snowflake for enterprise analytics and reporting
- Write and optimize complex SQL / PL‑SQL queries, procedures, and transformations to support large‑scale data processing
- Build and optimize enterprise data warehouse solutions on Snowflake.
- Develop data ingestion, transformation, and orchestration workflows using Python, PySpark, and Airflow.
- Develop and optimize complex SQL queries, stored procedures, views, streams, and tasks in Snowflake.
- Implement scalable cloud-based data solutions leveraging AWS services.
- Ensure data quality, governance, security, and compliance across the data platform.
- Perform query tuning, performance optimization, and cost optimization within Snowflake.
- Automate deployment and monitoring through CI/CD and DevOps practices.
- Collaborate with business stakeholders, architects, analysts, and engineering teams to translate business requirements into technical solutions.
- Troubleshoot production issues and implement preventive measures to enhance platform reliability.
Preferred Experience
- Experience in large-scale cloud data migration initiatives.
- Strong understanding of enterprise data architecture and modern data platforms.
- Experience working with structured, semi-structured, and unstructured data.
- Exposure to Banking, Financial Services, or other large enterprise environments will be an added advantage.
- Experience in leading technical discussions and mentoring engineering teams.
Similar Jobs
Information Technology
Leads the design, development, and support of enterprise data platforms and pipelines. Ingests, cleanses, models, and documents data from diverse sources into governed Kimball datasets for BI, machine learning, and AI. Oversees data engineers, ensures pipeline reliability and quality, applies DevOps and CI/CD practices, performs data analysis, and collaborates with stakeholders on requirements, governance, GDPR compliance, and continuous process improvement.
Top Skills:
Apache KafkaArmAzure CliAzure Data Factory V2Azure Data Lake Storage V2Azure DatabricksAzure DevopsAzure FunctionsAzure Logic AppsAzure PortalAzure PowershellAzure Resource ManagerAzure Stream AnalyticsBicepC#Ci/CdDelta LakeGitMicrosoft PurviewPower BIPowershellPysparkPytestPythonRest ApisSpark Structured StreamingSQLTerraformUnittestUnity CatalogYaml
eCommerce • Retail
Design and maintain scalable data pipelines and reusable platform tooling using Python, Scala, Spark, Azure, Databricks, and Delta Lake. Establish data engineering standards, CI/CD, data quality, testing, observability, metadata management, and performance optimization. Partner with data scientists, analysts, engineers, platform teams, and security stakeholders to deliver secure, reliable, cost-efficient, production-ready datasets across business domains.
Top Skills:
AzureAzure Data FactoryAzure Data Lake StorageCi/CdDatabricksDbtDelta LakePysparkPythonScalaSparkTerraform
Gaming
Leads the design, delivery, and optimization of scalable batch and real-time data platforms on GCP. Responsibilities include building pipelines, data modeling, orchestration, security, governance, monitoring, incident resolution, cost optimization, architectural decision-making, and system integrations. The role provides technical leadership, mentors engineers, translates complex requirements into data solutions, evaluates AI tooling, and owns the reliability, performance, and quality of business-critical data systems.
Top Skills:
AirflowBigQueryBigtableCloud ComposerCloud StorageDataflowDockerFirestoreGoogle Cloud Platform (Gcp)Pub/SubPythonSpannerSQLTerraformVertex Ai
What you need to know about the London Tech Scene
London isn't just a hub for established businesses; it's also a nursery for innovation. Boasting one of the most recognized fintech ecosystems in Europe, attracting billions in investments each year, London's success has made it a go-to destination for startups looking to make their mark. Top U.K. companies like Hoptin, Moneybox and Marshmallow have already made the city their base — yet fintech is just the beginning. From healthtech to renewable energy to cybersecurity and beyond, the city's startups are breaking new ground across a range of industries.
