Responsibilities :
Design and build data ingestion pipelines for files, APIs, SFTP sources, databases and enterprise data extracts.
Implement canonical data models, source-to-target mapping, transformation rules, validation checks, rejected-record handling and lineage capture.
Develop data quality checks, reconciliation routines, control result tables, exception datasets and metadata structures for auditable data processing.
Work with PostgreSQL, SQL, object storage, data lake patterns, ETL/ELT tools and batch processing frameworks as needed.
Partner with AI engineers to provide clean, contextual and grounded data for AI summaries, narratives, retrieval and explanations.
Create data dictionaries, mapping specifications, interface contracts, test datasets and data-quality documentation.
Support performance tuning, partitioning, indexing, data retention, archival and privacy-aware data handling.
Work with QA teams to defi
Technical and Professional Requirements:
Minimum 7 years of experience in data engineering, ETL/ELT development, data warehousing, data platform engineering or related roles.
Strong SQL skills and hands-on experience with relational databases such as PostgreSQL, SQL Server, Oracle or MySQL.
Experience designing ingestion pipelines, transformations, validation rules, data quality checks and source-to-target mapping specifications.
Understanding of data modeling, canonical models, metadata, lineage, audit fields, reconciliation and data quality concepts.
Experience with Python, Spark, Databricks, Azure Data Factory, dbt, Airflow or equivalent data engineering tools is preferred.
Ability to work with structured, semi-structured and file-based data formats such as CSV, JSON, Excel, XML and Parquet.
Strong documentation discipline and ability to collaborate with functional SMEs and engineers on data requirements.
