Key Responsibilities
- Design, build, and maintain scalable data pipelines for batch and real-time processing using Python, SQL, ETL/ELT frameworks, and big-data technologies.
- Participate in end-to-end data project delivery using SDLC, Agile, or hybrid development methodologies.
- Work closely with business and technology stakeholders to understand data requirements related to banking products, transactions, customer analytics, and regulatory reporting.
- Develop efficient normalized and de-normalized data models for operational and analytical workloads.
- Design and manage data warehouses, data marts, and integration layers aligned with enterprise data architecture.
- Deploy physical data models and optimize performance for large-scale financial datasets.
- Ensure adherence to data governance, quality, metadata, and privacy standards across all solutions.
- Produce and maintain data documentation including dictionaries, lineage diagrams, and technical specifications.
- Support data lineage, metadata management, and data quality initiatives to improve transparency and trust.
- Provide data-driven assistance to business users and proactively communicate technical challenges.
- Present insights, designs, and concepts effectively to both technical and non-technical stakeholders.
Skills/Experience:
Technical Skills
- Proficient in Python; experienced with Spark for scalable ETL/ELT pipelines.
- Strong SQL experience with large-scale datasets and warehouse solutions.
- Knowledge of Hadoop ecosystem tools such as Hive, Spark, and HDFS.
- Experience with AWS services including Glue, Redshift, RDS, S3, and basic IAM/VPC/security configurations.
- Hands-on Linux skills, shell scripting, and AWS CLI usage.
- Ability to work across SQL, NoSQL, and data lake environments.
- Exposure to Terraform, Talend, or similar tools is a plus.
- Familiarity with visualization tools such as QuickSight, Qlik, or Tableau.
- Ability to write clean, production-grade code and maintain clear pipeline documentation.
Experience
- Experience with large datasets on platforms such as Greenplum, Hadoop, Oracle, DB2, or MongoDB.
- Familiarity with dashboarding tools (Tableau, Power BI, SAS VA).
- Experience in scripting, application packaging, and deployment across DEV–PROD environments.
- Understanding of change management, service request processes, and maintenance reporting.
- Strong data modelling capabilities (logical and physical) for banking, risk, compliance, and analytics use cases.
- Deep knowledge of relational/dimensional modelling, data warehousing concepts, and data integration techniques.
- Strong SQL expertise supporting large and complex financial data environments.
Education & Certifications
- Bachelor’s degree in Software Engineering, Computer Science, or equivalent experience.
- Professional cloud certifications (AWS/Azure/GCP) are preferred, including:
- AWS Certified Data Analytics – Specialty
- AWS Solutions Architect – Associate
- Azure Data Engineer Associate
- Google Professional Data Engineer
- Databricks Data Engineer Associate/Professional
- Cloudera Certified Data Engineer
