Software Development Senior Specialist

Req ID:

387111

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now. We are currently seeking a Software Development Senior Specialist to join our team in GDL, Jalisco (MX-JAL), Mexico (MX). ROLE SUMMARY Data engineer specialised in moving data from heterogeneous source systems into Amazon S3 and processing it across the lake, with AWS Glue as the core engine. The primary focus of the role is ingestion: connectivity to sources, full and incremental extraction, landing zone design, and reliable, repeatable loads at scale. On top of that foundation, capabilities across every layer of the medallion architecture, from raw landing through cleansed and gold layer. Accountable for pipeline reliability, data quality and processing cost efficiency in enterprise or highly regulated environments. REQUIRED TECHNICAL SKILLS Source to S3 Ingestion (primary focus) • Source connectivity: JDBC extraction from Oracle, Redshift, SQL Server, including driver configuration, connection pooling, partitioned reads through bounded queries, and tuning of fetch size and parallelism to control source impact. • Extraction patterns: initial full loads and historical backfills, incremental extraction by watermark, timestamp or sequence, change data capture with AWS DMS, and idempotent, replayable loads that tolerate reruns without duplicating data. • Non relational sources: flat and hierarchical files (CSV, JSON, XML, Avro, fixed width). • Batch and near real time: ingestion with a difference in volume and frequency of data of refresh. • S3 landing zone design: bucket and prefix strategy, partition layout by date and business key, file format and compression choices (Parquet, ORC, Avro), target file sizing and compaction, lifecycle policies and immutability of raw data. • Reliability and control: schema drift detection and controlled evolution, row count and checksum reconciliation against the source, watermark and control tables, rejected record quarantine, and restartable loads with clear failure semantics. AWS Glue · Core Engine • Glue ETL development: PySpark job development in Glue Studio and script mode, DynamicFrames and Spark DataFrames, resolveChoice and schema mapping transforms, and reusable, parameterised job libraries. • Orchestration and state: job bookmarks for incremental processing, triggers and Glue Workflows, retries and failure handling, and integration with Step Functions and EventBridge for end to end pipeline orchestration. • Performance and cost tuning: worker autoscaling, partition and file size optimisation, predicate pushdown. • Data quality: AWS Glue Data Quality with DQDL rulesets, validation checkpoints embedded in pipelines, quarantine of rejected records, and publication of quality metrics. • Additional capabilities: Interactive Sessions and notebooks for development, Glue DataBrew for profiling and preparation, and job monitoring with CloudWatch logs, metrics and alarms. • Alerting and notification: EventBridge rules on job state changes, SNS and email notifications, integration with ticketing tools such as ServiceNow aligned to severity. • Cost observability: tracking of DPU hours and cost per job and per dataset, detection of cost anomalies, and identification of candidates for Flex execution or job refactoring. • Failure handling by design: retry and backoff strategies, checkpointing. • Incident management: triage and severity classification, resolution within agreed SLAs at L2 and L3, root cause analysis, and problem and change management for recurring failures. Analytical Processing Across All Layers • Raw and bronze: preservation of source reliability, technical metadata and audit columns, ingestion timestamps and batch identifiers, and lineage from file to load run. • Silver and curated: cleansing, standardisation, deduplication, referential validation, type and format harmonisation, and conformance of records arriving from multiple source systems. • Distributed processing at scale: Spark transformations, joins over large datasets, window functions, aggregations, pivots, deduplication by key and late arriving data handling, with partition and skew management. • Medallion architecture: clear ownership of the responsibilities of each layer, idempotent and replayable processing, and consistent promotion of data between zones. AWS Data Ecosystem • Storage and consumption: S3 as the lake foundation, Amazon Redshift . Complementary Tooling • Informatica: IDMC (Cloud Data Integration, Cloud Mass Ingestion) or PowerCenter for enterprise ingestion and data quality alongside Glue pipelines. • Denodo: understanding of the virtualization and semantic layer as a downstream consumer, and of how curated datasets are exposed as governed views and data products. • Engineering practices: Python and advanced SQL, Git based workflows, CI/CD pipelines (GitHub Actions, Jenkins or CodePipeline), infrastructure as code with Terraform or CloudFormation, and unit and integration testing of pipelines. KEY RESPONSIBILITIES • Design, build and maintain ingestion pipelines that move data from relational, file, API and streaming sources into Amazon S3, with full, incremental and CDC based loads. • Define and maintain the landing and raw zone layout: partitioning, file formats, naming conventions, compression, retention and immutability of source data. • Onboard new source systems end to end, covering connectivity and network configuration, credential management, extraction strategy, volume and window analysis, and coordination with source owners. • Guarantee load completeness and integrity through reconciliation against the source, control tables, reprocessing procedures and clear handling of failed or partial runs. • Implement processing across all layers of the medallion architecture, applying cleansing, standardisation, deduplication, conformance and business rules at the appropriate stage. • Register and maintain datasets in the Glue Data Catalog, keeping schemas, partitions and metadata aligned with Athena, Redshift and Lake Formation. • Tune job performance and cost, monitoring DPU consumption, execution times and file layout, and refactoring jobs that exceed agreed targets. • Embed data quality controls into pipelines, define rulesets, handle rejected records and publish quality metrics to stakeholders. • Orchestrate multi step pipelines with Glue Workflows, Step Functions and EventBridge, including dependency management, alerting and recovery. • Provide L2 and L3 support for production pipelines: incident analysis, root cause investigation, backfills and continuous improvement backlog. • Apply and document development standards, naming conventions and promotion procedures across Dev, QA and Production environments, and collaborate with governance and security teams on access policies, lineage capture and metadata quality. Languages • Working proficiency in English and Spanish (written and spoken). CERTIFICATIONS (DESIRABLE) • AWS Certified Data Engineer Associate (core certification for this role). • AWS Certified Solutions Architect Associate. • AWS Certified Developer Associate. • Informatica Cloud Data Integration Developer, Professional Certification (ICP). • Denodo Platform 9 Certified Developer Associate. • Databricks Certified Data Engineer Associate, or an equivalent Apache Spark certification. • Oracle Database SQL Certified Associate or equivalent. About NTT DATA NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R&D. Whenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client’s needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. 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