About Us

Capco a Wipro company is a global technology and management consulting firm. Awarded with Consultancy of the year in the British Bank Award and has been ranked Top 100 Best Companies for Women in India 2022 by Avtar & Seramount. With our presence across 32 cities across globe we support 100 clients acrossbanking financial and Energy sectors. We are recognized for our deep transformation execution and delivery.

WHY JOIN CAPCO

You will work on engaging projects with the largest international and local banks insurance companies payment service providers and other key players in the industry. The projects that will transform the financial services industry.

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Innovative thinking delivery excellence and thought leadership to help our clients transform their business. Together with our clients and industry partners we deliver disruptive work that is changing energy and financial services.

#BEYOURSELFATWORK

Capco has a tolerant open culture that values diversity inclusivity and creativity.

CAREER ADVANCEMENT

With no forced hierarchy at Capco everyone has the opportunity to grow as we grow taking their career into their own hands.

DIVERSITY & INCLUSION

We believe that diversity of people and perspective gives us a competitive advantage.

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Job Title: Senior Data Engineering - AWS

Location: Bangalore

Experience: 12 Years

• The data management platform is required to gather key data elements (payments notifications and status) from Kafka topics and store that data in raw form in AWS S3 (likely using S3 Tables/Iceberg). From the raw form the data needs to be curated via Glue to be available for queries via Athena. The target concept is an S3 Tables/Iceberg based Data Lakehouse.

  From this Data Lakehouse a number of data products will be created including payments and statements support enquiry and analysis capabilities.

  The engagement has these main phases:

  Analysis of As Is particularly on data gaps in current offerings Data Product definition in partnership with product owners Definition of To Be HLSD Multiple LLSD Recruit and lead implementation team Deployment and integration The target delivery dates are during 2026 with a start date in 2025.

  Email from Manish Tiwari below.

  Thanks Nathan Our base job requirement is around this we will interview all profiles shared

  Data Hands-On Architect Payments Data Products (BDD)

  Mission
  Build the data backbone for Bankline Direct. Ingest ISO 20022 lifecycle events (VPM/PMN/PSN) into an AWS lakehouse deliver sub-10 ms status queries for channel ops and 7 year analytics with strong governance observability and cost control.

  Own / Deliver

  • Data products (To-Be):Channel Ops Warehouse (30-day high-perf layer) and Channel Analytics Lake (7 yrs). ExposestatusandstatementsAPIs with clear SLAs.
  • Platform architecture:S3/Glue/Athena/Iceberg lakehouse Redshift for BI/ops. QuickSight for PO/ops dashboards. Lambda/Step Functions for stream processing orchestration.
  • Streaming & ingest:Kafka (K4/K5/Confluent) and AWS MSK/Kinesis; connectors/CDC to DW/Lake. Partitioning retention replay idempotency. EventBridge for AWS-native event routing.
  • Event contracts:Avro/Protobuf Schema Registry compatibility rules versioning strategy.
  • As-Is To-Be:Inventory APIs/File/SWIFT feeds and stores (Aurora Postgres Kafka). Define migration waves cutover runbooks.
  • Governance & quality:Data-as-a-product ownership lineage access controls quality rules retention.
  • Observability & FinOps:Grafana/Prometheus/CloudWatch for TPS success rate lag spend per 1M events. Runbooks actionable alerts.
  • Scale & resilience:Tens of millions of payments/day multi-AZ/region patterns pragmatic RPO/RTO.
  • Security:Data classification KMS encryption tokenization where needed least-privilege IAM immutable audit.
  • Hands-on build:Python/Scala/SQL; Spark/Glue; Step Functions/Lambda; IaC (Terraform); CI/CD (GitLab/Jenkins); automated tests.

  Must-Have Skills

  • Streaming & EDA:Kafka (Confluent) and AWS MSK/Kinesis/Kinesis Firehose; outbox ordering replay exactly/at-least-once semantics. EventBridge for event routing and filtering.
  • Schema management:Avro/Protobuf Schema Registry (compatibility subject strategy evolution).
  • AWS data stack:S3/Glue/Athena Redshift Step Functions Lambda; Iceberg-ready lakehouse patterns. KinesisS3Glue streaming pipelines; Glue Streaming; DLQ patterns.
  • Payments & ISO 20022:PAIN/PACS/CAMT lifecycle modeling reconciliation/advices; API/File/SWIFT channel knowledge.
  • Governance:Data-mesh mindset; ownership quality SLAs access retention lineage.
  • Observability & FinOps:Build dashboards alerts and cost KPIs; troubleshoot lag/throughput at scale.
  • Delivery:Production code performance profiling code reviews automated tests secure by design.

  Data Architecture Fundamentals (Must-Have)

  • Logical data modeling:Entity-relationship diagrams normalization (1NF through Boyce-Codd/BCNF) denormalization trade-offs; identify functional dependencies and key anomalies.
  • Physical data modeling:Table design partitioning strategies indexes; SCD types; dimensional vs. transactional schemas; storage patterns for OLTP vs. analytics.
  • Normalization & design:Normalize to 3NF/BCNF for OLTP; understand when to denormalize for queries; trade-offs between 3NF Data Vault and star schemas.
  • CQRS (Command Query Responsibility Segregation):Separate read/write models; event sourcing and state reconstruction; eventual consistency patterns; when CQRS is justified vs. overkill.
  • Event-Driven Architecture (EDA):Event-first design; aggregate boundaries and invariants; publish/subscribe patterns; saga orchestration; idempotency and at-least-once delivery.
  • Bounded contexts & domain modeling:Core/supporting/generic subdomains; context maps (anti-corruption layers shared kernel conformist published language); ubiquitous language.
  • Entities value objects & repositories:Domain entity identity; immutability for value objects; repository abstraction over persistence; temporal/versioned records.
  • Domain events & contracts:Schema versioning (Avro/Protobuf); backward/forward compatibility; event replay; mapping domain events to Kafka topics and Aurora tables.

  Nice-to-Have

  • QuickSight/Tableau; Redshift tuning; ksqlDB/Flink; Aurora Postgres internals.
  • Edge/API constraints (Apigee/API-GW) mTLS/webhook patterns.

  Operating Model

  • Act asdata product owner/architectwith CX/Payments/CPO.
  • Maintain backlog SLAs contracts.
  • Publish runbooks SLOs and quarterly cost/quality reports.