Jobs at Anblicks

Manages the IT Problem Management lifecycle by investigating incidents, producing root cause analysis reports, coordinating post-incident reviews, tracking metrics, identifying trends, and assigning corrective actions. Partners with Incident Management, Operations, Engineering, Development, and client teams to reduce major incident frequency and duration. Presents findings and performance metrics to technical leadership while facilitating cross-functional communication and continuous improvement.

Analyze complex datasets, develop and optimize SQL queries and Databricks workflows, perform data validation and quality checks, monitor production workflows, and build data quality dashboards. The role delivers actionable insights, supports automation and data governance, resolves data issues, and collaborates with engineering, business, vendor, and cross-functional stakeholders to improve data products and reporting.

Provides Tier 3 production support for enterprise applications, data platforms, pipelines, and cloud environments. Troubleshoots complex application, data, platform, and performance issues; performs root-cause analysis; implements permanent fixes; and supports major incidents, deployments, monitoring, and automation. The role works across Databricks, Snowflake, Cloudera, Dagster, dbt, AWS, GCP, and related technologies while collaborating with engineering, DevOps, data, product, and client-facing teams.

Design and implement scalable test automation frameworks, automated functional and API tests, database validation, and CI/CD integrations. Collaborate with engineering, DevOps, architecture, and product teams to improve software quality, testability, performance, security, and reliability. Troubleshoot complex technical issues, perform root-cause analysis, and support quality engineering throughout the software development lifecycle.

Develop and maintain responsive web applications using React.js and JavaScript. Build reusable components, integrate front-end systems with RESTful APIs, collaborate with UX/UI designers, optimize performance, troubleshoot issues, participate in code reviews, and follow software development best practices.

Manages database systems to ensure availability, performance, security, and data integrity. Responsibilities include DBMS installation and maintenance, performance tuning, backups and disaster recovery, schema and data model collaboration, SQL and stored procedure optimization, troubleshooting, and security policy implementation. The role also evaluates cloud and NoSQL database technologies and database automation tools.

Designs end-to-end GCP data platforms using BigQuery, GCS, ingestion pipelines, dimensional models, Power BI, and governance controls. Assesses existing data environments, defines architecture and standards, plans scalability and cost management, establishes security and data quality frameworks, and produces architecture blueprints, roadmaps, estimates, and implementation guidance. Partners with stakeholders and executives to facilitate workshops, explain trade-offs, and act as design authority during delivery.

Lead Agile delivery for AI, machine learning, generative AI, data engineering, and analytics programs. Facilitate Scrum ceremonies, coach teams, remove impediments, manage risks and dependencies, track delivery metrics, and coordinate cross-functional stakeholders. Support AI/ML lifecycle planning, release execution, continuous improvement, Agile transformation, and transparent reporting across distributed teams.

Design and develop scalable Go backend services, REST APIs, microservices, and cloud-native applications. Integrate databases and enterprise systems, deploy with Docker and Kubernetes, troubleshoot production issues, and contribute to architecture, testing, code reviews, observability, performance, and reliability improvements. Collaborate with DevOps and product teams on high-scale distributed systems.

Owns end-to-end delivery for strategic data engineering client engagements, including scope, timelines, quality, budget, risks, governance, and outcomes. Serves as the primary senior client contact, leads executive communications, manages scope and account growth, and provides technical oversight of data architecture, pipelines, quality, and engineering practices. Leads distributed cross-functional teams, supports hiring and performance management, and ensures healthy engagement margins and predictable delivery.

Leads the design, development, deployment, and monitoring of production-grade machine learning and statistical models for predictive, propensity, segmentation, and AI applications. The role manages the full data science lifecycle, builds scalable ML and MLOps pipelines using Python, Spark, and cloud platforms, explores GenAI and AI agents, defines performance metrics, communicates findings to stakeholders and clients, and mentors junior data scientists.

Design and develop autonomous agentic AI and multi-agent systems using LLMs, RAG, tool calling, and enterprise integrations. Build scalable workflows with Python and frameworks such as LangChain and LangGraph, implement guardrails and observability, optimize production deployments, and collaborate on architecture and business requirements. The role also includes mentoring junior engineers and supporting AI governance, security, reliability, and cost optimization.

Lead the design and management of secure, scalable AWS infrastructure and DevOps automation. Own CI/CD pipelines, infrastructure as code, container orchestration, monitoring, security, disaster recovery, and cost optimization. Troubleshoot complex production issues, drive DevSecOps practices, collaborate across engineering and security teams, and mentor DevOps engineers while providing technical leadership on architecture and operational improvements.

Leads strategic relationships with complex enterprise clients across healthcare, insurance, and media/marketing. Owns account health, planning, revenue forecasting, renewals, upsell and cross-sell growth, executive stakeholder engagement, proposals, contract negotiations, and delivery coordination. Mentors account managers, contributes to client engagement methodology, represents the firm at industry events, and provides strategic guidance within assigned industries.

Leads end-to-end delivery of software, data engineering, analytics, cloud, Snowflake, and Databricks initiatives. Defines scope, timelines, resources, budgets, risks, and success criteria; coordinates distributed teams, vendors, stakeholders, releases, and production deployments. Translates business needs into technical workstreams and Agile requirements, facilitates ceremonies, reviews technical designs, manages changes, resolves issues, and provides executive reporting.

Designs and builds scalable data platforms, Lakehouse architectures, ETL/ELT pipelines, data models, and cloud-native solutions using Databricks, PySpark, Python, SQL, and AWS. Responsibilities include Spark optimization, governance, security, data quality, observability, CI/CD, testing, DataOps, production issue resolution, architectural recommendations, and technical leadership across globally distributed teams.

Create and maintain product and technical documentation, including manuals, guides, SOPs, release notes, FAQs, and knowledge base articles. Collaborate with Product, Engineering, Data, and AI teams to understand technical concepts and translate them into clear, user-friendly content. Research, edit, and maintain accurate documentation while using AI tools to improve productivity and quality. The role also involves independently exploring products and emerging technologies.

Designs, builds, and operates scalable enterprise applications using Java, React, Python, microservices, and cloud platforms. Owns architecture and technical direction, integrates generative AI capabilities, leads complex delivery, and collaborates with data science teams on production AI services. Mentors engineers, drives testing and DevSecOps practices, resolves complex production issues, improves security, and communicates technical trade-offs across product, design, and executive stakeholders.

Design, build, and optimize scalable ETL/ELT pipelines and distributed data platforms using Databricks, Apache Spark, Python, Java, and SQL. Develop ingestion, transformation, validation, monitoring, and data quality frameworks for large datasets. Tune Spark jobs and SQL queries, troubleshoot production issues, support cloud-based architectures, and collaborate with cross-functional teams. The role also involves mentoring junior engineers and contributing to engineering standards, governance, security, and operational excellence.

Develop secure, scalable Java applications integrated with Snowflake and the Snowflake Native App Framework. Build Native App packages, manifests, roles, permissions, upgrades, APIs, SQL objects, and Snowpark solutions. Optimize performance and costs, implement testing and CI/CD, integrate cloud and enterprise systems, troubleshoot complex issues, and contribute to architecture, security, code reviews, and technical mentorship.