Senior Data Engineer

Job Description:

Required a results-driven Senior Data Engineer with experience in designing, developing, and delivering enterprise-scale data engineering solutions across data warehousing, big data, and cloud platforms.

Key Responsibilities & Leadership -

Design and implement enterprise-scale data architectures, including Data Warehouses and Data Lakes.

Build and maintain scalable ETL/ELT pipelines for analytics, reporting, and operational workloads.

Lead end-to-end data migration and modernisation programs from legacy systems to Hadoop, Spark, Databricks, and AWS platforms using Agile methodology.

Define and execute migration strategies, including data analysis, mapping, transformation, integration, validation, reconciliation, and production deployment.

Design and optimise data models, SQL queries, and Spark workloads to improve performance, scalability, and cost efficiency.

Develop automation solutions to reduce manual effort and improve operational efficiency.

Ensure data quality, consistency, governance, and compliance across enterprise data platforms.

Perform data profiling, validation, and reconciliation to ensure accuracy and reliability of data assets.

Lead technical design reviews, architecture discussions, and code reviews to ensure adherence to best practices and standards.

Manage full project lifecycle delivery, including requirements gathering, design, development, testing, deployment, and production support.

Collaborate with architects, business analysts, product owners, and clients to translate business requirements into technical solutions.

Lead and mentor data engineering teams, including task allocation, skill development, and performance management.

Identify and resolve technical and delivery blockers to ensure timely project execution.

Manage stakeholder expectations and communicate technical solutions clearly to both technical and non-technical audiences.

Deliver both new development and production support projects in enterprise environments.

Continuously improve systems by identifying opportunities for performance, scalability, and cost optimisation.

Adapt quickly to new technologies, including AI-enabled data platforms, modern data engineering tools, and evolving coding methodologies.

Apply modern coding practices, including modular design, reusable components, version control standards, and CI/CD-aligned development approaches.

Maintain a strong continuous learning mindset and ability to work across evolving data and AI-driven ecosystems.

Strong domain expertise in banking domain.

Technical Skills -

Data Engineering & Platforms: Data Warehousing, Data Lakes, ETL/ELT, Data Pipelines, Data Modelling, Data Profiling, Data Migration, Data Modernisation, Data Quality, Data Governance

Databases: SQL, Teradata, Oracle, DB2, Vertica, PostgreSQL, Amazon Redshift

Big Data Technologies: Hadoop ecosystem (HDFS, Hive, Hive SQL), Apache Spark, Spark SQL, PySpark

Cloud & Modern Data Platforms: AWS/Azure/GCP, Databricks/Snowflake/DBT

Programming & Scripting: Python, UNIX Shell Scripting

CI/CD: GitHub/ Jenkins

Pay: $100,000.00 – $150,000.00 per year

Work Location: In person